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Jiao Y, Zhang JZ, Zhao Q, Liu JQ, Wu ZZ, Li Y, Li H, Fu WL, Weng JC, Huo R, Zhao SZ, Wang S, Cao Y, Zhao JZ. Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography. Transl Stroke Res 2021; 13:939-948. [PMID: 34383209 DOI: 10.1007/s12975-021-00933-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 07/04/2021] [Accepted: 07/25/2021] [Indexed: 11/25/2022]
Abstract
The diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three experienced neuroradiologists delineated the bAVM lesions and identified the diffuseness on TOF-MRA images, which were considered the ground-truth reference. The U-Net-based segmentation model was trained to segment lesion areas. Eight mainstream ML models were trained through the radiomic features of segmented lesions to identify diffuseness, based on which an integrated model was built and yielded the best performance. In the test set, the Dice score, F2 score, precision, and recall for the segmentation model were 0.80 [0.72-0.84], 0.80 [0.71-0.86], 0.84 [0.77-0.93], and 0.82 [0.69-0.89], respectively. For the diffuseness identification model, the ensemble-based model was applied with an area under the Receiver-operating characteristic curves (AUC) of 0.93 (95% CI 0.87-0.99) in the training set. The AUC, accuracy, precision, recall, and F1 score for the diffuseness identification model were 0.95, 0.90, 0.81, 0.84, and 0.83, respectively, in the test set. The ML models showed good performance in automatically detecting bAVM lesions and identifying diffuseness. The method may help to judge the diffuseness of bAVMs objectively, quantificationally, and efficiently.
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Affiliation(s)
- Yuming Jiao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Jun-Ze Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Qi Zhao
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
| | - Jia-Qi Liu
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
| | - Zhen-Zhou Wu
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
| | - Yan Li
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
| | - Hao Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Wei-Lun Fu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Jian-Cong Weng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Ran Huo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Shao-Zhi Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Shuo Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
| | - Yong Cao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China.
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China.
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China.
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China.
| | - Ji-Zong Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Road West, Fengtai District, Beijing, People's Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, People's Republic of China
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Payette K, de Dumast P, Kebiri H, Ezhov I, Paetzold JC, Shit S, Iqbal A, Khan R, Kottke R, Grehten P, Ji H, Lanczi L, Nagy M, Beresova M, Nguyen TD, Natalucci G, Karayannis T, Menze B, Bach Cuadra M, Jakab A. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 2021; 8:167. [PMID: 34230489 PMCID: PMC8260784 DOI: 10.1038/s41597-021-00946-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 05/13/2021] [Indexed: 11/09/2022] Open
Abstract
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.
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Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
| | - Priscille de Dumast
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Hamza Kebiri
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ivan Ezhov
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Johannes C Paetzold
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Suprosanna Shit
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Asim Iqbal
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Center for Intelligent Systems & Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Romesa Khan
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, UZH/ETH Zurich, Zurich, Switzerland
| | - Raimund Kottke
- Department of Diagnostic Imaging, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Patrice Grehten
- Department of Diagnostic Imaging, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Levente Lanczi
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Marianna Nagy
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Monika Beresova
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Thi Dao Nguyen
- Newborn Research, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Giancarlo Natalucci
- Newborn Research, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
- Larsson-Rosenquist Center for Neurodevelopment, Growth and Nutrition of the Newborn, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
| | | | - Bjoern Menze
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Meritxell Bach Cuadra
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
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53
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Zhuang Y, Liu H, Song E, Ma G, Xu X, Hung CC. APRNet: A 3D Anisotropic Pyramidal Reversible Network with Multi-modal Cross-Dimension Attention for Brain Tissue Segmentation in MR Images. IEEE J Biomed Health Inform 2021; 26:749-761. [PMID: 34197331 DOI: 10.1109/jbhi.2021.3093932] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between brain tissue regions, automatic brain tissue segmentation without prior knowledge is still challenging. This paper presents a novel 3D fully convolutional network (FCN) for brain tissue segmentation, called APRNet. In this network, we first propose a 3D anisotropic pyramidal convolutional reversible residual sequence (3DAPC-RRS) module to integrate the intra-slice information with the inter-slice information without significant memory consumption; secondly, we design a multi-modal cross-dimension attention (MCDA) module to automatically capture the effective information in each dimension of multi-modal images; then, we apply 3DAPC-RRS modules and MCDA modules to a 3D FCN with multiple encoded streams and one decoded stream for constituting the overall architecture of APRNet. We evaluated APRNet on two benchmark challenges, namely MRBrainS13 and iSeg-2017. The experimental results show that APRNet yields state-of-the-art segmentation results on both benchmark challenge datasets and achieves the best segmentation performance on the cerebrospinal fluid region. Compared with other methods, our proposed approach exploits the complementary information of different modalities to segment brain tissue regions in both adult and infant MR images, and it achieves the average Dice coefficient of 87.22% and 93.03% on the MRBrainS13 and iSeg-2017 testing data, respectively. The proposed method is beneficial for quantitative brain analysis in the clinical study, and our code is made publicly available.
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Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021; 204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/11/2022]
Abstract
Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.
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Affiliation(s)
- Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
| | - Ole A Andreassen
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tomáš Paus
- ECOGENE-21, Chicoutimi, Quebec, Canada; Department of Psychology and Psychiatry, University of Toronto, Ontario, Canada; Department of Psychiatry and Centre hospitalier universitaire Sainte-Justine, University of Montreal, Canada
| | - Lars T Westlye
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
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55
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Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021; 226:1961-1972. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022]
Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohui Yan
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Kui Li
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Li Zhao
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Yu Zou
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
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56
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Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation. SENSORS 2021; 21:s21093232. [PMID: 34067101 PMCID: PMC8124734 DOI: 10.3390/s21093232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 11/17/2022]
Abstract
Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.
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57
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Chiarelli AM, Sestieri C, Navarra R, Wise RG, Caulo M. Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses. Hum Brain Mapp 2021; 42:3593-3607. [PMID: 33955622 PMCID: PMC8249887 DOI: 10.1002/hbm.25456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/09/2021] [Accepted: 04/11/2021] [Indexed: 12/27/2022] Open
Abstract
Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.
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Affiliation(s)
- Antonio M Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Riccardo Navarra
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Richard G Wise
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
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58
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Structural Changes in the Cortico-Ponto-Cerebellar Axis at Birth are Associated with Abnormal Neurological Outcomes in Childhood. Clin Neuroradiol 2021; 31:1005-1020. [PMID: 33944956 DOI: 10.1007/s00062-021-01017-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 04/04/2021] [Indexed: 10/21/2022]
Abstract
White matter lesions in hypoxic-ischemic encephalopathy (HIE) are considered to be the important substrate of frequent neurological consequences in preterm infants. The aim of the study was to analyze volumes and tractographic parameters of the cortico-ponto-cerebellar axis to assess alterations in the periventricular fiber system and crossroads, corticopontine and corticospinal pathways and prospective transsynaptic changes of the cerebellum.Term infants (control), premature infants without (normotypic) and with perinatal HIE (HIE) underwent brain magnetic resonance imaging at term-equivalent age (TEA) and at 2 years. Cerebrum, cerebellum, brainstem divisions and ventrodorsal compartments volumetric analysis were performed, as well as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of corticopontine, corticospinal pathways and middle cerebellar peduncles. Amiel-Tison scale at TEA and the Hempel test at 2 years were assessed.Cerebellum, brainstem and its compartments volumes were decreased in normotypic and HIE groups at TEA, while at 2 years volumes were significantly reduced in the HIE group, accompanied by decreased volume and FA and increased ADC of corticopontine and corticospinal pathways. Negative association of the brainstem, cerebellum, mesencephalon, pons, corticopontine volumes and corticospinal pathway FA at TEA with the neurological score at 2 years. Cerebellum and pons volumes presented as potential prognostic indicators of neurological outcomes.Our findings agree that these pathways, as a part of the periventricular fiber system and crossroads, exhibit lesion-induced reaction and vulnerability in HIE. Structural differences between normotypic and HIE group at the 2 years suggest a different developmental structural plasticity.
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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60
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Dou H, Karimi D, Rollins CK, Ortinau CM, Vasung L, Velasco-Annis C, Ouaalam A, Yang X, Ni D, Gholipour A. A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1123-1133. [PMID: 33351755 PMCID: PMC8016740 DOI: 10.1109/tmi.2020.3046579] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.
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61
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Moura LM, Ferreira VLDR, Loureiro RM, de Paiva JPQ, Rosa-Ribeiro R, Amaro E, Soares MBP, Machado BS. The Neurobiology of Zika Virus: New Models, New Challenges. Front Neurosci 2021; 15:654078. [PMID: 33897363 PMCID: PMC8059436 DOI: 10.3389/fnins.2021.654078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/08/2021] [Indexed: 12/21/2022] Open
Abstract
The Zika virus (ZIKV) attracted attention due to one striking characteristic: the ability to cross the placental barrier and infect the fetus, possibly causing severe neurodevelopmental disruptions included in the Congenital Zika Syndrome (CZS). Few years after the epidemic, the CZS incidence has begun to decline. However, how ZIKV causes a diversity of outcomes is far from being understood. This is probably driven by a chain of complex events that relies on the interaction between ZIKV and environmental and physiological variables. In this review, we address open questions that might lead to an ill-defined diagnosis of CZS. This inaccuracy underestimates a large spectrum of apparent normocephalic cases that remain underdiagnosed, comprising several subtle brain abnormalities frequently masked by a normal head circumference. Therefore, new models using neuroimaging and artificial intelligence are needed to improve our understanding of the neurobiology of ZIKV and its true impact in neurodevelopment.
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Affiliation(s)
| | | | | | | | | | - Edson Amaro
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Milena Botelho Pereira Soares
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ), Bahia, Brazil.,University Center SENAI CIMATEC, SENAI Institute of Innovation (ISI) in Advanced Health Systems (CIMATEC ISI SAS), National Service of Industrial Learning - SENAI, Bahia, Brazil
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62
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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63
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Turesky TK, Vanderauwera J, Gaab N. Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life. Dev Cogn Neurosci 2021; 47:100893. [PMID: 33341534 PMCID: PMC7750693 DOI: 10.1016/j.dcn.2020.100893] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/21/2020] [Accepted: 12/05/2020] [Indexed: 12/20/2022] Open
Abstract
Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.
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Affiliation(s)
- Ted K Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Jolijn Vanderauwera
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Psychological Sciences Research Institute, Université Catholique De Louvain, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université Catholique De Louvain, Louvain-la-Neuve, Belgium
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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64
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Khawam M, de Dumast P, Deman P, Kebiri H, Yu T, Tourbier S, Lajous H, Hagmann P, Maeder P, Thiran JP, Meuli R, Dunet V, Bach Cuadra M, Koob M. Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study. Front Pediatr 2021; 9:639746. [PMID: 34447726 PMCID: PMC8383736 DOI: 10.3389/fped.2021.639746] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/07/2021] [Indexed: 11/27/2022] Open
Abstract
We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2). Twenty-five normal fetal brain MRI scans (18-34 weeks of gestation) including orthogonal 3-mm-thick T2WSs were analyzed retrospectively. One 3D SR volume was reconstructed per subject based on multiple series of T2WSs. The two observers performed 11 2D biometric measurements (specifying their level of confidence) on T2WS and SR volumes. Measurements were compared using the paired Wilcoxon rank sum test between observers for each dataset (T2WS and SR) and between T2WS and SR for each observer. Bland-Altman plots were used to assess the agreement between each pair of measurements. Measurements were made with low confidence in three subjects by obs1 and in 11 subjects by obs2 (mostly concerning the length of the corpus callosum on T2WS). Inter-rater intra-dataset comparisons showed no significant difference (p > 0.05), except for brain axial biparietal diameter (BIP) on T2WS and for brain and skull coronal BIP and coronal transverse cerebellar diameter (DTC) on SR. None of them remained significant after correction for multiple comparisons. Inter-dataset intra-rater comparisons showed statistical differences in brain axial and coronal BIP for both observers, skull coronal BIP for obs1, and axial and coronal DTC for obs2. After correction for multiple comparisons, only axial brain BIP remained significantly different, but differences were small (2.95 ± 1.73 mm). SR allows similar fetal brain biometry as compared to using the conventional T2WS while improving the level of confidence in the measurements and using a single reconstructed volume.
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Affiliation(s)
- Marie Khawam
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Pierre Deman
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Thomas Yu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sébastien Tourbier
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Hélène Lajous
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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65
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Chen J, Fang Z, Zhang G, Ling L, Li G, Zhang H, Wang L. Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework. Comput Med Imaging Graph 2020; 88:101848. [PMID: 33385932 DOI: 10.1016/j.compmedimag.2020.101848] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 11/15/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022]
Abstract
Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we propose a novel deep learning-based multi-step framework for brain extraction from 3D fetal MR images. In the first step, a global localization network is applied to estimate probability maps for brain candidates. Connected-component labeling algorithm is applied to eliminate small erroneous components and accurately locate the candidate brain area. In the second step, a local refinement network is implemented in the brain candidate area to obtain fine-grained probability maps. Final extraction results are derived by a fusion network with the two cascaded probability maps obtained from previous two steps. Experimental results demonstrate that our proposed method has superior performance compared with existing deep learning-based methods.
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Affiliation(s)
- Jian Chen
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, 350118, China.
| | - Zhenghan Fang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27517, USA
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Lei Ling
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27517, USA
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China.
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27517, USA.
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66
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Cai S, Zhang G, Zhang H, Wang J. Normative linear and volumetric biometric measurements of fetal brain development in magnetic resonance imaging. Childs Nerv Syst 2020; 36:2997-3005. [PMID: 32468242 DOI: 10.1007/s00381-020-04633-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 04/16/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To provide normative two-dimensional and three-dimensional measurements of brain development in normal fetuses during the second and third trimester by a new semi-automated method. METHODS In this retrospective study, we included 98 normal fetuses at our institution between 21 and 38 weeks of gestation. Two-dimensional measurements of the brain were including biparietal diameter, occipitofrontal diameter, head circumference, transverse cerebellar diameter, and atrial diameter. Volumetric parameters were obtained by using ITK-SNAP software, including left and right cerebral hemispheres, lateral ventricle, the cerebellum, and extracerebral cerebrospinal fluid. RESULTS All linear and volume measurements were positively correlated with gestational age except for cerebrospinal fluid. Each anatomical region of the fetal brain showed a different relative growth rate. There was some volume asymmetry between the left and right lateral ventricles, and the left side was larger. The inter-observer and intra-observer agreement was excellent for all measures. CONCLUSION We established the 5th, 50th, and 95th percentile values of fetal brain volume measurements in magnetic resonance, and this may be helpful to understand the damage of fetal brain development.
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Affiliation(s)
- Shulei Cai
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China.
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jing Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
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67
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Segmentation of MRI brain scans using spatial constraints and 3D features. Med Biol Eng Comput 2020; 58:3101-3112. [PMID: 33155095 DOI: 10.1007/s11517-020-02270-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 09/08/2020] [Indexed: 10/23/2022]
Abstract
This paper presents a novel unsupervised algorithm for brain tissue segmentation in magnetic resonance imaging (MRI). The proposed algorithm, named Gardens2, adopts a clustering approach to segment voxels of a given MRI into three classes: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using an overlapping criterion, 3D feature descriptors and prior atlas information, Gardens2 generates a segmentation mask per class in order to parcellate the brain tissues. We assessed our method using three neuroimaging datasets: BrainWeb, IBSR18, and IBSR20, the last two provided by the Internet Brain Segmentation Repository. Its performance was compared with eleven well established as well as newly proposed unsupervised segmentation methods. Overall, Gardens2 obtained better segmentation performance than the rest of the methods in two of the three databases and competitive results when its performance was measured by class. Graphical Abstract Brain tissue segmentation using 3D features and an adjusted atlas template.
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68
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Collins-Jones LH, Arichi T, Poppe T, Billing A, Xiao J, Fabrizi L, Brigadoi S, Hebden JC, Elwell CE, Cooper RJ. Construction and validation of a database of head models for functional imaging of the neonatal brain. Hum Brain Mapp 2020; 42:567-586. [PMID: 33068482 PMCID: PMC7814762 DOI: 10.1002/hbm.25242] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 12/17/2022] Open
Abstract
The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.
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Affiliation(s)
- Liam H Collins-Jones
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK.,Department of Bioengineering, Imperial College of Science, Technology, and Medicine, London, UK
| | - Tanya Poppe
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Addison Billing
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Institute for Cognitive Neuroscience, University College London, London, UK
| | - Jiaxin Xiao
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Lorenzo Fabrizi
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sabrina Brigadoi
- Department of Information Engineering, University of Padova, Padova, Italy.,Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy
| | - Jeremy C Hebden
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Clare E Elwell
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Robert J Cooper
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
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69
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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Katušić A, Raguž M, Žunić Išasegi I. Brain tissue volumes at term-equivalent age are associated with early motor behavior in very preterm infants. Int J Dev Neurosci 2020; 80:409-417. [PMID: 32433785 DOI: 10.1002/jdn.10039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/06/2020] [Accepted: 05/13/2020] [Indexed: 11/10/2022] Open
Abstract
Preterm birth is associated with a wide range of adverse developmental outcomes, including sensory, motor, cognitive and language impairments, and behavioral or attention problems. Subtle motor deficits that might emerge in premature infants with no evident or with mild brain injury encompass qualitative and quantitative aspects of motor behavior. This prospective cohort study provided an evaluation of the relationship between brain tissue volumes revealed by magnetic resonance imaging (MRI) at term-equivalent age and motor behavior in infancy in very preterm infants (total number = 40; mean gestational age = 28 weeks + 4 days; mean birth weight = 1190 g) without evident or with mild brain injury. Infants were recruited at birth and assessed at 12 months corrected age using the tool for qualitative and quantitative assessment of motor behavior, infant motor profile. The brain tissue was segmented first using advanced segmentation techniques and the volumes were measured by summing the volumes of all voxels belonging to a particular tissue class. The associations between volumetric brain MRI measures with motor behavior were explored using linear regression analyses. Results showed that larger total brain volumes were associated with higher motor score. Similar relationships were documented for parietal lobe, deep gray matter, and cerebellum volumes. Volumetric quantitative data of brain structures may serve as biomarkers for subtle motor deficits described in very preterm born infants without or with mild brain lesions apparent on MRI.
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Affiliation(s)
- Ana Katušić
- Croatian Institute for Brain Research, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Marina Raguž
- Department of Neurosurgery, School of Medicine, University Hospital Dubrava, University of Zagreb, Zagreb, Croatia
| | - Iris Žunić Išasegi
- Croatian Institute for Brain Research, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
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71
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Turesky T, Xie W, Kumar S, Sliva DD, Gagoski B, Vaughn J, Zöllei L, Haque R, Kakon SH, Islam N, Petri WA, Nelson CA, Gaab N. Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: A pilot study. Neuroimage 2020; 210:116540. [PMID: 31945509 PMCID: PMC7068701 DOI: 10.1016/j.neuroimage.2020.116540] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/06/2019] [Accepted: 01/10/2020] [Indexed: 01/03/2023] Open
Abstract
Anthropometric indicators, including stunting, underweight, and wasting, have previously been associated with poor neurocognitive outcomes. This link may exist because malnutrition and infection, which are known to affect height and weight, also impact brain structure according to animal models. However, a relationship between anthropometric indicators and brain structural measures has not been tested yet, perhaps because stunting, underweight, and wasting are uncommon in higher-resource settings. Further, with diminished anthropometric growth prevalent in low-resource settings, where biological and psychosocial hazards are most severe, one might expect additional links between measures of poverty, anthropometry, and brain structure. To begin to examine these relationships, we conducted an MRI study in 2-3-month-old infants growing up in the extremely impoverished urban setting of Dhaka, Bangladesh. The sample size was relatively small because the challenges of investigating infant brain structure in a low-resource setting needed to be realized and resolved before introducing a larger cohort. Initially, fifty-four infants underwent T1 sequences using 3T MRI, and resulting structural images were segmented into gray and white matter maps, which were carefully evaluated for accurate tissue labeling by a pediatric neuroradiologist. Gray and white matter volumes from 29 infants (79 ± 10 days-of-age; F/M = 12/17), whose segmentations were of relatively high quality, were submitted to semi-partial correlation analyses with stunting, underweight, and wasting, which were measured using height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) scores. Positive semi-partial correlations (after adjusting for chronological age and sex and correcting for multiple comparisons) were observed between white matter volume and HAZ and WAZ; however, WHZ was not correlated with any measure of brain volume. No associations were observed between income-to-needs or maternal education and brain volumetric measures, suggesting that measures of poverty were not associated with total brain tissue volume in this sample. Overall, these results provide the first link between diminished anthropometric growth and white matter volume in infancy. Challenges of conducting a developmental neuroimaging study in a low-resource country are also described.
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Affiliation(s)
- Ted Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Wanze Xie
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Swapna Kumar
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Danielle D Sliva
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Jennifer Vaughn
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Rashidul Haque
- The International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Nazrul Islam
- National Institute of Neuroscience and Hospital, Dhaka, Bangladesh
| | - William A Petri
- Division of Infectious Diseases and International Health, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Charles A Nelson
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard Graduate School of Education, Cambridge, MA, United States
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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72
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Delannoy Q, Pham CH, Cazorla C, Tor-Díez C, Dollé G, Meunier H, Bednarek N, Fablet R, Passat N, Rousseau F. SegSRGAN: Super-resolution and segmentation using generative adversarial networks - Application to neonatal brain MRI. Comput Biol Med 2020; 120:103755. [PMID: 32421654 DOI: 10.1016/j.compbiomed.2020.103755] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 04/07/2020] [Accepted: 04/07/2020] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. METHODS Our strategy mainly relies on generative adversarial networks. The network architecture is described in detail. We provide information about its implementation, focusing on the most crucial technical points (whereas complementary details are given in a dedicated GitHub repository). We illustrate the behavior of the proposed method for cortex analysis from neonatal MR images. RESULTS The results of the method, evaluated quantitatively (Dice, peak signal-to-noise ratio, structural similarity, number of connected components) and qualitatively on a research dataset (dHCP) and a clinical one (Epirmex), emphasize the relevance of the approach, and its ability to take advantage of data-augmentation strategies. CONCLUSIONS Results emphasize the potential of our proposed method/software with respect to practical medical applications. The method is provided as a freely available software tool, which allows one to carry out his/her own experiments, and involve the method for the super-resolution reconstruction and segmentation of arbitrary cerebral structures from any MR image dataset.
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Affiliation(s)
- Quentin Delannoy
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France
| | - Chi-Hieu Pham
- IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France
| | - Clément Cazorla
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France
| | | | - Guillaume Dollé
- Université de Reims Champagne Ardenne, CNRS, LMR UMR 9008, 51097 Reims, France
| | - Hélène Meunier
- Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France
| | - Nathalie Bednarek
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France; Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France
| | - Ronan Fablet
- IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France
| | - Nicolas Passat
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France.
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73
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Alexander B, Yang JYM, Yao SHW, Wu MH, Chen J, Kelly CE, Ball G, Matthews LG, Seal ML, Anderson PJ, Doyle LW, Cheong JLY, Spittle AJ, Thompson DK. White matter extension of the Melbourne Children's Regional Infant Brain atlas: M-CRIB-WM. Hum Brain Mapp 2020; 41:2317-2333. [PMID: 32083379 PMCID: PMC7267918 DOI: 10.1002/hbm.24948] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 01/29/2020] [Accepted: 02/02/2020] [Indexed: 11/05/2022] Open
Abstract
Brain atlases providing standardised identification of neonatal brain regions are key in investigating neurological disorders of early childhood. Our previously developed Melbourne Children's Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 brain regions including cortical, subcortical, and cerebellar regions. The aim of this study was to extend M-CRIB atlas coverage to include 54 white matter (WM) regions. Participants were 10 healthy term-born neonates that were used to create the initial M-CRIB atlas. WM regions were manually segmented based on T2 images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions imitate the Johns Hopkins University neonatal atlas, with minor anatomical modifications. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy. The resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions, and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB cortical, subcortical, and cerebellar parcellations in 10 individual neonatal MRI data sets. The novel M-CRIB-WM atlas, along with the M-CRIB cortical and subcortical atlases, provide neonatal whole brain MRI coverage in the first multi-subject manually parcellated neonatal atlas compatible with atlases commonly used at older time points. The M-CRIB-WM atlas is publicly available, providing a valuable tool that will help facilitate neuroimaging research into neonatal brain development in both healthy and diseased states.
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Affiliation(s)
- Bonnie Alexander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Neurosurgery, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Hui Wen Yao
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Monash School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Michelle Hao Wu
- Medical Imaging, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Claire E Kelly
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Lillian G Matthews
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Peter J Anderson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alicia J Spittle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
| | - Deanne K Thompson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
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74
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Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders. Diagnostics (Basel) 2020; 10:diagnostics10010027. [PMID: 31936008 PMCID: PMC7169467 DOI: 10.3390/diagnostics10010027] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/01/2020] [Accepted: 01/05/2020] [Indexed: 11/16/2022] Open
Abstract
The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.
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75
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Mostapha M, Styner M. Role of deep learning in infant brain MRI analysis. Magn Reson Imaging 2019; 64:171-189. [PMID: 31229667 PMCID: PMC6874895 DOI: 10.1016/j.mri.2019.06.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/06/2019] [Accepted: 06/08/2019] [Indexed: 12/17/2022]
Abstract
Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them.
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Affiliation(s)
- Mahmoud Mostapha
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America.
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America; Neuro Image Research and Analysis Lab, Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, United States of America.
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76
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Khalili N, Lessmann N, Turk E, Claessens N, Heus RD, Kolk T, Viergever M, Benders M, Išgum I. Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn Reson Imaging 2019; 64:77-89. [DOI: 10.1016/j.mri.2019.05.020] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 05/04/2019] [Accepted: 05/15/2019] [Indexed: 10/26/2022]
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77
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Khalili N, Turk E, Benders MJNL, Moeskops P, Claessens NHP, de Heus R, Franx A, Wagenaar N, Breur JMPJ, Viergever MA, Išgum I. Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks. NEUROIMAGE-CLINICAL 2019; 24:102061. [PMID: 31835284 PMCID: PMC6909142 DOI: 10.1016/j.nicl.2019.102061] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 10/24/2019] [Accepted: 10/26/2019] [Indexed: 01/21/2023]
Abstract
Automatic intracranial volume segmentation. Fetal and neonatal MRI. Deep learning.
MR images of infants and fetuses allow non-invasive analysis of the brain. Quantitative analysis of brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). Fast changes in the size and morphology of the developing brain, motion artifacts, and large variation in the field of view make ICV segmentation a challenging task. We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. The method was developed and tested with a diverse set of scans regarding image acquisition parameters (i.e. field strength, image acquisition plane, image resolution), infant age (23–45 weeks post menstrual age), and pathology (posthaemorrhagic ventricular dilatation, stroke, asphyxia, and Down syndrome). The results demonstrate that the method achieves accurate segmentation with a Dice coefficient (DC) ranging from 0.98 to 0.99 in neonatal and fetal scans regardless of image acquisition parameters or patient characteristics. Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans.
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Affiliation(s)
- Nadieh Khalili
- Image Sciences Institute, Utrecht University and University Medical Center Utrecht, Utrecht, the Netherlands.
| | - E Turk
- Department of Neonatology, Wilhelmina Childrens Hospital, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - M J N L Benders
- Department of Neonatology, Wilhelmina Childrens Hospital, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - P Moeskops
- Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands
| | - N H P Claessens
- Department of Neonatology, Wilhelmina Childrens Hospital, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - R de Heus
- Department of Obstetrics, University Medical Center Utrecht, the Netherlands
| | - A Franx
- Department of Obstetrics, University Medical Center Utrecht, the Netherlands
| | - N Wagenaar
- Department of Neonatology, Wilhelmina Childrens Hospital, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - J M P J Breur
- Department of Neonatology, Wilhelmina Childrens Hospital, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - M A Viergever
- Image Sciences Institute, Utrecht University and University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - I Išgum
- Image Sciences Institute, Utrecht University and University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
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78
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Attallah O, Sharkas MA, Gadelkarim H. Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age. Brain Sci 2019; 9:brainsci9090231. [PMID: 31547368 PMCID: PMC6770437 DOI: 10.3390/brainsci9090231] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 08/30/2019] [Accepted: 09/08/2019] [Indexed: 02/07/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Maha A Sharkas
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Heba Gadelkarim
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
- Department of Computer and Communication Engineering (SSP), Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt.
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79
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Pishghadam M, Kazemi K, Nekooei S, Seilanian-Toosi F, Hoseini-Ghahfarokhi M, Zabizadeh M, Fatemi A. A new approach to automatic fetal brain extraction from MRI using a variational level set method. Med Phys 2019; 46:4983-4991. [PMID: 31419312 DOI: 10.1002/mp.13766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 07/30/2019] [Accepted: 07/31/2019] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Appropriate images extracted from the MRI of mothers' wombs can be of great help in the medical diagnosis of fetal abnormalities. As maternal tissue may appear in such images, affecting visualization of myelination of the fetal brain, it is not possible to use methods routinely used for extraction of adult brains for fetal brains. The aim of the present study was to use a variational level set approach to extract fetal brain from T2-weighted MR images of the womb. METHODS Coronal T2-weighted images were acquired using fast MRI protocols (to avoid artifacts). The database includes 105 MR images from eight subjects. After correcting the inhomogeneity of the images, the fetal eyes were located, and from that information, the location of the fetus brain was automatically determined. Then, the variational level set was used for fetus brain extraction. The results were analyzed by a clinical specialist (radiologist) and the similarity (Dice and Jaccard coefficients), sensitivity and specificity were calculated. RESULTS AND CONCLUSIONS The means of the statistical analysis for the Dice and Jaccard coefficients, sensitivity and specificity, were 99.56%, 96.89%, 95.71%, and 97.96%, respectively. Thus, extraction of fetal brain from MR images was confirmed, both statistically and visually through cross-validation.
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Affiliation(s)
- Morteza Pishghadam
- Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Sirous Nekooei
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farrokh Seilanian-Toosi
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mojtaba Hoseini-Ghahfarokhi
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mansour Zabizadeh
- Department of Radiology and Nuclear Medicine, School of Para Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Fatemi
- Department of Radiation Oncology and Radiology, University of Mississippi Medical Center (UMMC), Jackson, MS, USA
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80
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Turesky TK, Jensen SK, Yu X, Kumar S, Wang Y, Sliva DD, Gagoski B, Sanfilippo J, Zöllei L, Boyd E, Haque R, Hafiz Kakon S, Islam N, Petri WA, Nelson CA, Gaab N. The relationship between biological and psychosocial risk factors and resting-state functional connectivity in 2-month-old Bangladeshi infants: A feasibility and pilot study. Dev Sci 2019; 22:e12841. [PMID: 31016808 PMCID: PMC6713583 DOI: 10.1111/desc.12841] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 01/25/2023]
Abstract
Childhood poverty has been associated with structural and functional alterations in the developing brain. However, poverty does not alter brain development directly, but acts through associated biological or psychosocial risk factors (e.g. malnutrition, family conflict). Yet few studies have investigated risk factors in the context of infant neurodevelopment, and none have done so in low-resource settings such as Bangladesh, where children are exposed to multiple, severe biological and psychosocial hazards. In this feasibility and pilot study, usable resting-state fMRI data were acquired in infants from extremely poor (n = 16) and (relatively) more affluent (n = 16) families in Dhaka, Bangladesh. Whole-brain intrinsic functional connectivity (iFC) was estimated using bilateral seeds in the amygdala, where iFC has shown susceptibility to early life stress, and in sensory areas, which have exhibited less susceptibility to early life hazards. Biological and psychosocial risk factors were examined for associations with iFC. Three resting-state networks were identified in within-group brain maps: medial temporal/striatal, visual, and auditory networks. Infants from extremely poor families compared with those from more affluent families exhibited greater (i.e. less negative) iFC in precuneus for amygdala seeds; however, no group differences in iFC were observed for sensory area seeds. Height-for-age, a proxy for malnutrition/infection, was not associated with amygdala/precuneus iFC, whereas prenatal family conflict was positively correlated. Findings suggest that it is feasible to conduct infant fMRI studies in low-resource settings. Challenges and practical steps for successful implementations are discussed.
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Affiliation(s)
- Ted K. Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Sarah K.G. Jensen
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Xi Yu
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Swapna Kumar
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
| | - Yingying Wang
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
- College of Education and Human SciencesUniversity of Nebraska‐LincolnLincolnNebraska
| | - Danielle D. Sliva
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Department of NeuroscienceBrown UniversityProvidenceRhode Island
| | - Borjan Gagoski
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Joseph Sanfilippo
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMassachusetts
| | - Emma Boyd
- A.A. Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMassachusetts
| | - Rashidul Haque
- The International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | | | - Nazrul Islam
- National Institute of Neurosciences & HospitalDhakaBangladesh
| | - William A. Petri
- Division of Infectious Diseases and International Health, Department of Medicine, School of MedicineUniversity of VirginiaCharlottesvilleVirginia
| | - Charles A. Nelson
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
- Harvard Graduate School of EducationCambridgeMassachusetts
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
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81
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Vasung L, Charvet CJ, Shiohama T, Gagoski B, Levman J, Takahashi E. Ex vivo fetal brain MRI: Recent advances, challenges, and future directions. Neuroimage 2019; 195:23-37. [PMID: 30905833 PMCID: PMC6617515 DOI: 10.1016/j.neuroimage.2019.03.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 03/12/2019] [Accepted: 03/16/2019] [Indexed: 12/21/2022] Open
Abstract
During early development, the fetal brain undergoes dynamic morphological changes. These changes result from neurogenic events, such as neuronal proliferation, migration, axonal elongation, retraction, and myelination. The duration and intensity of these events vary across species. Comparative assessments of these neurogenic events give us insight into evolutionary changes and the complexity of human brain development. Recent advances in magnetic resonance imaging (MRI), especially ex vivo MRI, permit characterizing and comparing fetal brain development across species. Comparative ex vivo MRI studies support the detection of species-specific differences that occur during early brain development. In this review, we provide a comprehensive overview of ex vivo MRI studies that characterize early brain development in humans, monkeys, cats, as well as rats/mice. Finally, we discuss the current advantages and limitations of ex vivo fetal brain MRI.
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Affiliation(s)
- Lana Vasung
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA
| | - Christine J Charvet
- Department of Molecular Biology and Genetics, Cornell University, 526 Campus Rd, Ithaca, NY, 14850, USA; Department of Psychology, Delaware State University, Dover, DE, 19901, USA
| | - Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA; Department of Pediatrics, Chiba University Hospital, Inohana 1-8-1, Chiba-shi, Chiba, 2608670, Japan
| | - Borjan Gagoski
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA; Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA.
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82
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Age-specific optimization of T1-weighted brain MRI throughout infancy. Neuroimage 2019; 199:387-395. [PMID: 31154050 DOI: 10.1016/j.neuroimage.2019.05.075] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 05/10/2019] [Accepted: 05/28/2019] [Indexed: 12/16/2022] Open
Abstract
The infant brain undergoes drastic morphological and functional development during the first year of life. Three-dimensional T1-weighted Magnetic Resonance Imaging (3D T1w-MRI) is a major tool to characterize the brain anatomy, which however, manifests inherently low and rapidly changing contrast between white matter (WM) and gray matter (GM) in the infant brains (0-12 month-old). Despite the prior efforts made to maximize tissue contrast in the neonatal brains (≤1 months), optimization of imaging methods in the rest of the infancy (1-12 months) is not fully addressed, while brains in the latter period exhibit even more challenging contrast. Here, we performed a systematic investigation to improve the contrast between cortical GM and subcortical WM throughout the infancy. We first performed simultaneous T1 and proton density mapping in a normally developing infant cohort at 3T (n = 57). Based on the evolution of T1 relaxation times, we defined three age groups and simulated the relative tissue contrast between WM and GM in each group. Age-specific imaging strategies were proposed according to the Bloch simulation: inversion time (TI) around 800 ms for the 0-3 month-old group, dual TI at 500 ms and 700 ms for the 3-7 month-old group, and TI around 700 ms for 7-12 month-old group, using a centrically encoded 3D-MPRAGE sequence at 3T. Experimental results with varying TIs in each group confirmed improved contrast at the proposed optimal TIs, even in 3-7 month-old infants who had nearly isointense contrast. We further demonstrated the advantage of improved relative contrast in segmenting the neonatal brains using a multi-atlas segmentation method. The proposed age-specific optimization strategies can be easily adapted to routine clinical examinations, and the improved image contrast would facilitate quantitative analysis of the infant brain development.
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83
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Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ben Ayed I. HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1116-1126. [PMID: 30387726 DOI: 10.1109/tmi.2018.2878669] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on six month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available.
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84
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Orea-Flores IY, Gallegos-Funes FJ, Arellano-Reynoso A. Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising. ENTROPY (BASEL, SWITZERLAND) 2019; 21:e21040401. [PMID: 33267115 PMCID: PMC7514888 DOI: 10.3390/e21040401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/22/2019] [Accepted: 04/04/2019] [Indexed: 06/12/2023]
Abstract
In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.
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Affiliation(s)
- Izlian Y. Orea-Flores
- Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional Av. IPN s/n, Edificio Z, acceso 3, 3 piso; SEPI-Electrónica, Col. Lindavista, 07738 Ciudad de México, Mexico
| | - Francisco J. Gallegos-Funes
- Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional Av. IPN s/n, Edificio Z, acceso 3, 3 piso; SEPI-Electrónica, Col. Lindavista, 07738 Ciudad de México, Mexico
| | - Alfonso Arellano-Reynoso
- Instituto Nacional de Neurología y Neurocirugía, Av. Insurgentes Sur 3877, Col. La Farma, 14269 Ciudad de México, Mexico
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85
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Rasmussen JM, Graham AM, Entringer S, Gilmore JH, Styner M, Fair DA, Wadhwa PD, Buss C. Maternal Interleukin-6 concentration during pregnancy is associated with variation in frontolimbic white matter and cognitive development in early life. Neuroimage 2019; 185:825-835. [PMID: 29654875 PMCID: PMC6181792 DOI: 10.1016/j.neuroimage.2018.04.020] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 03/08/2018] [Accepted: 04/09/2018] [Indexed: 01/21/2023] Open
Abstract
Maternal inflammation during pregnancy can alter the trajectory of fetal brain development and increase risk for offspring psychiatric disorders. However, the majority of relevant research to date has been conducted in animal models. Here, in humans, we focus on the structural connectivity of frontolimbic circuitry as it is both critical for socioemotional and cognitive development, and commonly altered in a range of psychiatric disorders associated with intrauterine inflammation. Specifically, we test the hypothesis that elevated maternal concentration of the proinflammatory cytokine interleukin-6 (IL-6) during pregnancy will be associated with variation in microstructural properties of this circuitry in the neonatal period and across the first year of life. Pregnant mothers were recruited in early pregnancy and maternal blood samples were obtained for assessment of maternal IL-6 concentrations in early (12.6 ± 2.8 weeks [S.D.]), mid (20.4 ± 1.5 weeks [S.D.]) and late (30.3 ± 1.3 weeks [S.D.]) gestation. Offspring brain MRI scans were acquired shortly after birth (N = 86, scan age = 3.7 ± 1.7 weeks [S.D.]) and again at 12-mo age (N = 32, scan age = 54.0 ± 3.1 weeks [S.D.]). Diffusion Tensor Imaging (DTI) was used to characterize fractional anisotropy (FA) along the left and right uncinate fasciculus (UF), representing the main frontolimbic fiber tract. In N = 30 of the infants with serial MRI data at birth and 12-mo age, cognitive and socioemotional developmental status was characterized using the Bayley Scales of Infant Development. All analyses tested for potentially confounding influences of household income, prepregnancy Body-Mass-Index, obstetric risk, smoking during pregnancy, and infant sex, and outcomes at 12-mo age were additionally adjusted for the quality of the postnatal caregiving environment. Maternal IL-6 concentration (averaged across pregnancy) was prospectively and inversely associated with FA (suggestive of reduced integrity under high inflammatory conditions) in the newborn offspring (bi-lateral, p < 0.01) in the central portion of the UF proximal to the amygdala. Furthermore, maternal IL-6 concentration was positively associated with rate of FA increase across the first year of life (bi-lateral, p < 0.05), resulting in a null association between maternal IL-6 and UF FA at 12-mo age. Maternal IL-6 was also inversely associated with offspring cognition at 12-mo age, and this association was mediated by FA growth across the first year of postnatal life. Findings from the current study support the premise that susceptibility for cognitive impairment and potentially psychiatric disorders may be affected in utero, and that maternal inflammation may constitute an intrauterine condition of particular importance in this context.
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Affiliation(s)
- Jerod M Rasmussen
- Development, Health and Disease Research Program, University of California, 92697, Irvine, CA, USA; Department of Pediatrics, University of California, 92697, Irvine, CA, USA.
| | - Alice M Graham
- Department of Behavioral Neuroscience, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, United States
| | - Sonja Entringer
- Development, Health and Disease Research Program, University of California, 92697, Irvine, CA, USA; Department of Pediatrics, University of California, 92697, Irvine, CA, USA; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health (BIH), Institute of Medical Psychology, Berlin, Germany
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, 27599, North Carolina, USA
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, 27599, North Carolina, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, United States
| | - Pathik D Wadhwa
- Development, Health and Disease Research Program, University of California, 92697, Irvine, CA, USA; Department of Pediatrics, University of California, 92697, Irvine, CA, USA; Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, 92697, Irvine, CA, USA
| | - Claudia Buss
- Development, Health and Disease Research Program, University of California, 92697, Irvine, CA, USA; Department of Pediatrics, University of California, 92697, Irvine, CA, USA; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health (BIH), Institute of Medical Psychology, Berlin, Germany.
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86
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Automatic Fetal Brain Extraction Using Multi-stage U-Net with Deep Supervision. MACHINE LEARNING IN MEDICAL IMAGING 2019. [DOI: 10.1007/978-3-030-32692-0_68] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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87
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Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2018; 29:102-127. [PMID: 30553609 DOI: 10.1016/j.zemedi.2018.11.002] [Citation(s) in RCA: 713] [Impact Index Per Article: 118.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 02/06/2023]
Abstract
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
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Affiliation(s)
- Alexander Selvikvåg Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Norway.
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Norway.
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88
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Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, Ballester MÁG. Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects. Med Image Anal 2018; 51:61-88. [PMID: 30390513 DOI: 10.1016/j.media.2018.10.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 10/09/2018] [Accepted: 10/18/2018] [Indexed: 12/19/2022]
Abstract
Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.
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Affiliation(s)
- Jordina Torrents-Barrena
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Narcís Masoller
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Eduard Gratacós
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Mario Ceresa
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ángel González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
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89
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Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, O'Donnell LJ. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 2018; 179:429-447. [PMID: 29920375 PMCID: PMC6080311 DOI: 10.1016/j.neuroimage.2018.06.027] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/01/2018] [Accepted: 06/08/2018] [Indexed: 12/15/2022] Open
Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
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Affiliation(s)
- Fan Zhang
- Harvard Medical School, Boston, USA.
| | - Ye Wu
- Harvard Medical School, Boston, USA
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90
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An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Graph 2018; 70:73-82. [PMID: 30296626 DOI: 10.1016/j.compmedimag.2018.09.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/10/2018] [Accepted: 09/13/2018] [Indexed: 11/21/2022]
Abstract
Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.
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91
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Wang L, Li G, Adeli E, Liu M, Wu Z, Meng Y, Lin W, Shen D. Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp 2018; 39:2609-2623. [PMID: 29516625 PMCID: PMC5951769 DOI: 10.1002/hbm.24027] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 02/01/2018] [Accepted: 02/19/2018] [Indexed: 01/14/2023] Open
Abstract
Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.
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Affiliation(s)
- Li Wang
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Gang Li
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Ehsan Adeli
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Mingxia Liu
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Zhengwang Wu
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Yu Meng
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
- Department of Computer ScienceUniversity of North Carolina at Chapel HillNorth Carolina
| | - Weili Lin
- MRI Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Dinggang Shen
- IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
- Department of Brain and Cognitive EngineeringKorea UniversitySeoul02841Republic of Korea
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92
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The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities. Med Image Anal 2018; 48:75-94. [PMID: 29852312 DOI: 10.1016/j.media.2018.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 05/04/2018] [Accepted: 05/09/2018] [Indexed: 11/20/2022]
Abstract
Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to the usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.
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93
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Oishi K, Chang L, Huang H. Baby brain atlases. Neuroimage 2018; 185:865-880. [PMID: 29625234 DOI: 10.1016/j.neuroimage.2018.04.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/27/2018] [Accepted: 04/02/2018] [Indexed: 01/23/2023] Open
Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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94
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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95
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018. [PMID: 29409960 DOI: 10.1101/125526] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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96
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018; 173:88-112. [PMID: 29409960 DOI: 10.1016/j.neuroimage.2018.01.054] [Citation(s) in RCA: 240] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/11/2022] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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