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Karimi D, Calixto C, Snoussi H, Cortes-Albornoz MC, Velasco-Annis C, Rollins C, Jaimes C, Gholipour A, Warfield SK. Detailed delineation of the fetal brain in diffusion MRI via multi-task learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.29.609697. [PMID: 39257731 PMCID: PMC11383702 DOI: 10.1101/2024.08.29.609697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.
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Affiliation(s)
- Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Calixto
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Elmhurst Hospital Center and Icahn School of Medicine at Mount Sinai, New York, NY
| | - Haykel Snoussi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | | | | | - Caitlin Rollins
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Jaimes
- Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Department of Radiological Sciences, University of California Irvine, Irvine, CA
| | - Simon K Warfield
- Boston Children's Hospital and Harvard Medical School, Boston, MA
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Calixto C, Jaimes C, Soldatelli MD, Warfield SK, Gholipour A, Karimi D. Anatomically constrained tractography of the fetal brain. Neuroimage 2024; 297:120723. [PMID: 39029605 PMCID: PMC11382095 DOI: 10.1016/j.neuroimage.2024.120723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/03/2024] [Indexed: 07/21/2024] Open
Abstract
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.
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Affiliation(s)
- Camilo Calixto
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | | | - Simon K Warfield
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Ali Gholipour
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Davood Karimi
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA.
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Balaha HM, Ayyad SM, Alksas A, Shehata M, Elsorougy A, Badawy MA, Abou El-Ghar M, Mahmoud A, Alghamdi NS, Ghazal M, Contractor S, El-Baz A. Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI. Bioengineering (Basel) 2024; 11:629. [PMID: 38927865 PMCID: PMC11200510 DOI: 10.3390/bioengineering11060629] [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: 04/15/2024] [Revised: 05/22/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.
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Affiliation(s)
- Hossam Magdy Balaha
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Sarah M. Ayyad
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Mohamed Shehata
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Ali Elsorougy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Mohamed Ali Badawy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Ali Mahmoud
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Depatrment, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
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Patriat R, Palnitkar T, Chandrasekaran J, Sretavan K, Braun H, Yacoub E, McGovern RA, Aman J, Cooper SE, Vitek JL, Harel N. DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei. Front Hum Neurosci 2024; 18:1324710. [PMID: 38439939 PMCID: PMC10910100 DOI: 10.3389/fnhum.2024.1324710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/05/2024] [Indexed: 03/06/2024] Open
Abstract
The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.
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Affiliation(s)
- Rémi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Tara Palnitkar
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Jayashree Chandrasekaran
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Karianne Sretavan
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Henry Braun
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Robert A. McGovern
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States
| | - Joshua Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Scott E. Cooper
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States
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5
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Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, Huang SY, Tian Q. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal 2023; 86:102744. [PMID: 36867912 PMCID: PMC10517382 DOI: 10.1016/j.media.2023.102744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 12/25/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.
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Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Qiyuan Tian
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
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6
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Theaud G, Edde M, Dumont M, Zotti C, Zucchelli M, Deslauriers-Gauthier S, Deriche R, Jodoin PM, Descoteaux M. DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography. FRONTIERS IN NEUROIMAGING 2022; 1:917806. [PMID: 37555143 PMCID: PMC10406193 DOI: 10.3389/fnimg.2022.917806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/09/2022] [Indexed: 08/10/2023]
Abstract
Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose DORIS, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a "true" ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.
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Affiliation(s)
- Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
| | - Manon Edde
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | | | - Mauro Zucchelli
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte D'Azur, Nice, France
| | | | - Rachid Deriche
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte D'Azur, Nice, France
| | - Pierre-Marc Jodoin
- Imeka Solutions Inc., Sherbrooke, QC, Canada
- Videos & Images Theory and Analytics Laboratory (VITAL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
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Martin D, Tong E, Kelly B, Yeom K, Yedavalli V. Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview. FRONTIERS IN RADIOLOGY 2021; 1:713681. [PMID: 37492174 PMCID: PMC10365125 DOI: 10.3389/fradi.2021.713681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 07/21/2021] [Indexed: 07/27/2023]
Abstract
Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.
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Affiliation(s)
- Dann Martin
- Vanderbilt University, Nashville, TN, United States
| | - Elizabeth Tong
- Department of Neuroradiology, Stanford Health Care, Stanford, CA, United States
| | - Brendan Kelly
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Kristen Yeom
- Department of Neuroradiology, Stanford Health Care, Stanford, CA, United States
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Zopes J, Platscher M, Paganucci S, Federau C. Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks. Front Neurol 2021; 12:653375. [PMID: 34335436 PMCID: PMC8318570 DOI: 10.3389/fneur.2021.653375] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 06/17/2021] [Indexed: 11/17/2022] Open
Abstract
Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on T1-weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities. A large database of in total 853 MRI/CT brain scans enables us to train convolutional neural networks (CNNs) for segmentation. We benchmark the CNN performance on four different imaging modalities and 27 anatomical substructures. For each modality we train a separate CNN based on a common architecture. We find average Dice scores of 86.7 ± 4.1% (T1-weighted MRI), 81.9 ± 6.7% (fluid-attenuated inversion recovery MRI), 80.8 ± 6.6% (diffusion-weighted MRI) and 80.7 ± 8.2% (CT), respectively. The performance is assessed relative to labels obtained using the widely-adopted FreeSurfer software package. The segmentation pipeline uses dropout sampling to identify corrupted input scans or low-quality segmentations. Full segmentation of 3D volumes with more than 2 million voxels requires <1s of processing time on a graphical processing unit.
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Affiliation(s)
- Jonathan Zopes
- Institute for Biomedical Engineering, ETH Zürich, Zurich, Switzerland
| | - Moritz Platscher
- Institute for Biomedical Engineering, ETH Zürich, Zurich, Switzerland
| | - Silvio Paganucci
- Institute for Biomedical Engineering, ETH Zürich, Zurich, Switzerland
| | - Christian Federau
- Institute for Biomedical Engineering, ETH Zürich, Zurich, Switzerland
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Zhang F, Breger A, Cho KIK, Ning L, Westin CF, O'Donnell LJ, Pasternak O. Deep learning based segmentation of brain tissue from diffusion MRI. Neuroimage 2021; 233:117934. [PMID: 33737246 PMCID: PMC8139182 DOI: 10.1016/j.neuroimage.2021.117934] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/12/2020] [Accepted: 03/01/2021] [Indexed: 02/06/2023] Open
Abstract
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
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Affiliation(s)
- Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anna Breger
- Faculty of Mathematics, University of Vienna, Wien, Austria
| | - Kang Ik Kevin Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lipeng Ning
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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10
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Malovani C, Friedman N, Ben-Eliezer N, Tavor I. Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging. J Magn Reson Imaging 2021; 54:1066-1076. [PMID: 33894095 DOI: 10.1002/jmri.27654] [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: 02/09/2021] [Revised: 04/01/2021] [Accepted: 04/02/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. PURPOSE To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). STUDY TYPE Retrospective longitudinal study. POPULATION Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project. FIELD STRENGTH/SEQUENCE 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. ASSESSMENT A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. STATISTICAL TESTS Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. RESULTS The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). DATA CONCLUSION A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 1.
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Affiliation(s)
- Cfir Malovani
- School of Electrical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Naama Friedman
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Noam Ben-Eliezer
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Bio-Medical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, New York, USA
| | - Ido Tavor
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel
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Sparse representation of DWI images for fully automated brain tissue segmentation. J Neurosci Methods 2020; 343:108828. [PMID: 32603811 DOI: 10.1016/j.jneumeth.2020.108828] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 06/14/2020] [Accepted: 06/22/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Brain tissue segmentation plays an important role in biomedical research and clinical applications. Traditional segmentation is performed on T1-weighted and/or T2-weighted MRI images. Recently, brain segmentation based on diffusion weighted imaging (DWI) has attracted research interest due to its advantage in diffusion MRI image processing and anatomically-constrained tractography. NEW METHOD We propose a fully automated brain segmentation method based on sparse representation of DWI signals and applied it on nine healthy subjects of Human Connectome Project aged 25-35 years. Learning a dictionary from DWI signals of each subject, brain voxels are classified into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) according to their sparse representation of clustered dictionary atoms, achieving good agreement with the segmentation on T1-weighted images using SPM12, as assessed by the DICE score. RESULTS The average DICE score for all nine subjects was 0.814 for CSF, 0.850 for GM, and 0.890 for WM. The proposed method is very fast and robust for a wide range of sparse coding parameter selection. It also works well on DWI data with less number of shells or gradient directions. COMPARISON WITH EXISTING METHODS On average, our segmentation results are superior to previous methods for all three brain tissue classes in terms of DICE scores. CONCLUSION The proposed method demonstrates the feasibility of segmenting the brain solely based on the tissue response to diffusion encoding.
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Cheng H, Newman S, Afzali M, Fadnavis SS, Garyfallidis E. Segmentation of the brain using direction-averaged signal of DWI images. Magn Reson Imaging 2020; 69:1-7. [PMID: 32088291 DOI: 10.1016/j.mri.2020.02.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 02/10/2020] [Accepted: 02/18/2020] [Indexed: 12/25/2022]
Abstract
Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution.
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Affiliation(s)
- Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
| | | | - Eleftherios Garyfallidis
- Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA
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