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Obenaus A, Kinney-Lang E, Jullienne A, Haddad E, Wendel KM, Shereen AD, Solodkin A, Dunn JF, Baram TZ. Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala. Biomedicines 2023; 11:biomedicines11020535. [PMID: 36831071 PMCID: PMC9953214 DOI: 10.3390/biomedicines11020535] [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: 01/24/2023] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
The amygdaloid complex, including the basolateral nucleus (BLA), contributes crucially to emotional and cognitive brain functions, and is a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of the poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here, we describe a novel diffusion tensor imaging (DTI) approach to enhance contrast, enabling the optimal identification of BLA in the rodent brain from magnetic resonance (MR) images. We employed this methodology together with a slice-shifting approach to accurately measure BLA volumes. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed BLA connectivity targets using DTI-based tractography. The novel approach enables the accurate and reliable delineation of BLA. Because this nucleus is involved in and changed by developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.
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Affiliation(s)
- Andre Obenaus
- Department of Pediatrics, Loma Linda University School of Medicine, Loma Linda, CA 92350, USA
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
- Department of Anatomy/Neurobiology, University of California, Irvine, CA 92697, USA
- Correspondence:
| | - Eli Kinney-Lang
- Department of Pediatrics, Loma Linda University School of Medicine, Loma Linda, CA 92350, USA
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
| | - Amandine Jullienne
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
| | - Elizabeth Haddad
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
| | - Kara M. Wendel
- Department of Anatomy/Neurobiology, University of California, Irvine, CA 92697, USA
| | - A. Duke Shereen
- Department of Anatomy/Neurobiology, University of California, Irvine, CA 92697, USA
| | - Ana Solodkin
- Department of Anatomy/Neurobiology, University of California, Irvine, CA 92697, USA
- Department of Neurology, University of California, Irvine, CA 92697, USA
| | - Jeffrey F. Dunn
- Department of Radiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Alberta T2N 4N1, Canada
| | - Tallie Z. Baram
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
- Department of Anatomy/Neurobiology, University of California, Irvine, CA 92697, USA
- Department of Neurology, University of California, Irvine, CA 92697, USA
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Sundaresan V, Zamboni G, Rothwell PM, Jenkinson M, Griffanti L. Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images. Med Image Anal 2021; 73:102184. [PMID: 34325148 PMCID: PMC8505759 DOI: 10.1016/j.media.2021.102184] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/10/2021] [Accepted: 07/16/2021] [Indexed: 01/05/2023]
Abstract
White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts, and we also submitted our method to MWSC 2017 to be evaluated on the unseen test datasets. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning methods of MWSC 2017.
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Affiliation(s)
- Vaanathi Sundaresan
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, UK
- Oxford India Centre for Sustainable Development, Somerville College, University of Oxford, UK
| | - Giovanna Zamboni
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Universitá di Modena e Reggio Emilia, Italy
| | - Peter M. Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Australian Institute for Machine Learning (AIML), School of Computer Science, The University of Adelaide, Adelaide, Australia
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK
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Diniz PHB, Valente TLA, Diniz JOB, Silva AC, Gattass M, Ventura N, Muniz BC, Gasparetto EL. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 167:49-63. [PMID: 29706405 DOI: 10.1016/j.cmpb.2018.04.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 02/12/2018] [Accepted: 04/17/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND OBJECTIVE White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. METHODS The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. RESULTS The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. CONCLUSIONS It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.
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Affiliation(s)
- Pedro Henrique Bandeira Diniz
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Thales Levi Azevedo Valente
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - João Otávio Bandeira Diniz
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Nina Ventura
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
| | - Bernardo Carvalho Muniz
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
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Roura E, Sarbu N, Oliver A, Valverde S, González-Villà S, Cervera R, Bargalló N, Lladó X. Automated Detection of Lupus White Matter Lesions in MRI. Front Neuroinform 2016; 10:33. [PMID: 27570507 PMCID: PMC4981618 DOI: 10.3389/fninf.2016.00033] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Accepted: 07/25/2016] [Indexed: 01/14/2023] Open
Abstract
Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.
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Affiliation(s)
- Eloy Roura
- Department of Computer Architecture and Technology, University of Girona Girona, Spain
| | - Nicolae Sarbu
- Centre de Diagnòstic per la Imatge, Hospital Clínic Barcelona, Spain
| | - Arnau Oliver
- Department of Computer Architecture and Technology, University of Girona Girona, Spain
| | - Sergi Valverde
- Department of Computer Architecture and Technology, University of Girona Girona, Spain
| | - Sandra González-Villà
- Department of Computer Architecture and Technology, University of Girona Girona, Spain
| | - Ricard Cervera
- Department of Autoimmune Diseases, Hospital Clínic-Institut d'Investigació Biomèdica August Pi i Sunyer Barcelona, Spain
| | - Núria Bargalló
- Centre de Diagnòstic per la Imatge, Hospital ClínicBarcelona, Spain; Magnetic Resonance Imaging Core Facility, Institut d'Investigació Biomèdica August Pi i SunyerBarcelona, Spain
| | - Xavier Lladó
- Department of Computer Architecture and Technology, University of Girona Girona, Spain
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Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. Neuroinformatics 2016; 13:261-76. [PMID: 25649877 PMCID: PMC4468799 DOI: 10.1007/s12021-015-9260-y] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.
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6
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Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. Comput Med Imaging Graph 2015; 45:102-11. [PMID: 26398564 DOI: 10.1016/j.compmedimag.2015.08.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Revised: 08/08/2015] [Accepted: 08/18/2015] [Indexed: 11/24/2022]
Abstract
White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.
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Bianchi A, Bhanu B, Obenaus A. Dynamic Low-Level Context for the Detection of Mild Traumatic Brain Injury. IEEE Trans Biomed Eng 2015; 62:145-53. [DOI: 10.1109/tbme.2014.2342653] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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8
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Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 2014; 98:324-35. [DOI: 10.1016/j.neuroimage.2014.04.056] [Citation(s) in RCA: 119] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 03/26/2014] [Accepted: 04/21/2014] [Indexed: 11/17/2022] Open
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Ghosh N, Sun Y, Bhanu B, Ashwal S, Obenaus A. Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images. Med Image Anal 2014; 18:1059-69. [PMID: 25000294 DOI: 10.1016/j.media.2014.05.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 04/23/2014] [Accepted: 05/10/2014] [Indexed: 11/30/2022]
Abstract
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects.
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Affiliation(s)
- Nirmalya Ghosh
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA
| | - Yu Sun
- Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA
| | - Bir Bhanu
- Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA
| | - Stephen Ashwal
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA
| | - Andre Obenaus
- Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA; Cell, Molecular and Developmental Biology Program and Department of Neuroscience, University of California, 1140 Bachelor Hall, Riverside, CA 92521, USA.
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Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images. BIOMED RESEARCH INTERNATIONAL 2014; 2014:248505. [PMID: 24757667 PMCID: PMC3976812 DOI: 10.1155/2014/248505] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 02/01/2014] [Accepted: 02/05/2014] [Indexed: 02/06/2023]
Abstract
BACKGROUND The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project. METHODS With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies. RESULTS In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.). CONCLUSIONS CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita.
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Ithapu V, Singh V, Lindner C, Austin BP, Hinrichs C, Carlsson CM, Bendlin BB, Johnson SC. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies. Hum Brain Mapp 2014; 35:4219-35. [PMID: 24510744 DOI: 10.1002/hbm.22472] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 11/12/2013] [Accepted: 01/07/2014] [Indexed: 01/18/2023] Open
Abstract
Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies.
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Affiliation(s)
- Vamsi Ithapu
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin; Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
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Bianchi A, Bhanu B, Donovan V, Obenaus A. Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:11-22. [PMID: 23797243 DOI: 10.1109/tmi.2013.2269317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.
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Shi L, Wang D, Liu S, Pu Y, Wang Y, Chu WCW, Ahuja AT, Wang Y. Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction. J Neurosci Methods 2012; 213:138-46. [PMID: 23261771 DOI: 10.1016/j.jneumeth.2012.12.014] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Revised: 12/14/2012] [Accepted: 12/14/2012] [Indexed: 11/29/2022]
Abstract
PURPOSE It has been reported that increased white matter lesions (WML) is one of the risk factors for stroke. To quantify WML objectively with the presence of acute infarcts, we proposed an automated segmentation scheme to locate WMLs in combined T1-weighted MRI, fluid attenuation inversion recovery (FLAIR) and diffusion weighted imaging (DWI). MATERIALS AND METHODS The proposed method detects WMLs by a coarse-to-fine mathematical morphology method. It has been evaluated quantitatively and qualitatively using voxel-based, volume-based, score-based, and atlas-based approaches on MRI data of 91 subjects with acute infarction. RESULT The proposed WML detection algorithm yields average sensitivity, positive predictive value and similarity index of 0.803, 0.818, and 0.836, respectively. Experimental results demonstrated that the segmentation from the proposed method is in high agreement with that from manual segmentation (intraclass correlation coefficient=0.9892), and with a good correlation with visual scores (R=0.8442, p<0.0001).
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Affiliation(s)
- Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
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14
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Abdullah BA, Younis AA, John NM. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs. Open Biomed Eng J 2012. [DOI: 10.2174/1874120701206010056] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
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15
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Abdullah BA, Younis AA, John NM. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs. Open Biomed Eng J 2012; 6:56-72. [PMID: 22741026 PMCID: PMC3382289 DOI: 10.2174/1874230001206010056] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2011] [Revised: 11/11/2011] [Accepted: 12/23/2011] [Indexed: 11/24/2022] Open
Abstract
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
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Affiliation(s)
- Bassem A Abdullah
- Department of Electrical and Computer Engineering, University of Miami, Miami, Fl, USA
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Feasibility of texture analysis for the assessment of biochemical changes in meniscal tissue on T1 maps calculated from delayed gadolinium-enhanced magnetic resonance imaging of cartilage data: comparison with conventional relaxation time measurements. Invest Radiol 2011; 45:543-7. [PMID: 20661144 DOI: 10.1097/rli.0b013e3181ea363b] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To (1) establish the feasibility of texture analysis for the in vivo assessment of biochemical changes in meniscal tissue on delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), and (2) compare textural with conventional T1 relaxation time measurements calculated from dGEMRIC data ("T1(Gd) relaxation times"). MATERIALS AND METHODS We enrolled 10 asymptomatic volunteers (7 men and 3 women; mean age, 27.2 +/- 4.5 years), without a history of meniscus damage, in our study. MRI of the right knee was performed at 3.0 T. An isotropic, 3-dimensional (3D), double-echo steady-state sequences was used for morphologic evaluation, and a dual flip angle 3D gradient echo sequence was used for T1(Gd) mapping. All MRI scans were performed 90 minutes after injection of 0.2 mmol/kg of Gd-diethylenetriamine pentaacetic acid (DTPA), and subsequently, during application of a compressive force (50% of the body weight) in the axial direction. Regions of interest, covering the central portions of the posterior horn of the medial meniscus, were defined on 3 adjacent sagittal sections. Based on the relaxation time maps, mean T1(Gd), as well as the T1(Gd) texture features derived from the co-occurrence matrix (COC: Angular Second Moment, Entropy, Inverse Difference Moment) and wavelet transform (WAV: WavEnLL, WavEnHL, WavEnHH, WavEnLH), were calculated. Paired t tests were used to assess differences between baseline and compression, and intraclass correlation coefficients (ICC) were calculated to establish the intrarater reliability of the measurements. RESULTS Mean T1(Gd) (-67.3 ms, P = 0.011), Angular Second Moment (-0.0002, P = 0.009), Entropy (+0.033, P = 0.025), WavEnLL (+1011.16, P = 0.002), WavEnHL (+18.64, P = 0.012), and WavEnLH (+72.74, P = 0.035) differed significantly between baseline and compression. Intrarater reliability was substantial for mean T1(Gd) relaxation times (ICC = 0.99-1.0), and also for T1(Gd) co-occurrence matrix (ICC = 0.63-0.92) and WAV (ICC = 0.86-0.98) features. CONCLUSIONS Texture features extracted from T1 maps calculated from dGEMRIC data are feasible for the in vivo assessment of biochemical changes in the menisci, such as might be induced by mechanical loading. Thus, T1(Gd) texture features complement conventional relaxation time measurements. Further studies are necessary to determine whether the mechanical compression, or a prolonged Gd-DTPA uptake, or both, are responsible for the observed decrease in mean T1(Gd) relaxation times in the menisci.
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Abdullah BA, Younis AA, Pattany PM, Saraf-Lavi E. Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs. ACTA ACUST UNITED AC 2011. [DOI: 10.4236/ojmi.2011.12005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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18
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White matter lesion segmentation based on feature joint occurrence probability and random field theory from magnetic resonance (MR) images. Pattern Recognit Lett 2010. [DOI: 10.1016/j.patrec.2010.01.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Scully M, Anderson B, Lane T, Gasparovic C, Magnotta V, Sibbitt W, Roldan C, Kikinis R, Bockholt HJ. An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus. Front Hum Neurosci 2010; 4:27. [PMID: 20428508 PMCID: PMC2859868 DOI: 10.3389/fnhum.2010.00027] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2009] [Accepted: 03/11/2010] [Indexed: 11/29/2022] Open
Abstract
We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater.
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Affiliation(s)
- Mark Scully
- The Mind Research NetworkAlbuquerque, NM, USA
- Department of Computer Science, The University of New MexicoAlbuquerque, NM, USA
- Advanced Biomedical Informatics Group LLCIowa City, IA, USA
| | - Blake Anderson
- Department of Computer Science, The University of New MexicoAlbuquerque, NM, USA
| | - Terran Lane
- Department of Computer Science, The University of New MexicoAlbuquerque, NM, USA
| | - Charles Gasparovic
- The Mind Research NetworkAlbuquerque, NM, USA
- Department of Psychology, The University of New MexicoAlbuquerque, NM, USA
| | - Vince Magnotta
- Radiology Department, Carver School of Medicine, The University of IowaIowa City, IA, USA
| | - Wilmer Sibbitt
- Rheumatology, Department of Internal Medicine, School of Medicine, The University of New MexicoAlbuquerque, NM, USA
| | - Carlos Roldan
- Cardiology, Department of Internal Medicine, School of Medicine, The University of New MexicoAlbuquerque, NM, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard School of MedicineBoston, MA, USA
| | - Henry J. Bockholt
- The Mind Research NetworkAlbuquerque, NM, USA
- Advanced Biomedical Informatics Group LLCIowa City, IA, USA
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Kruggel F, Turner J, Muftuler LT. Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 2009; 49:2123-33. [PMID: 19913626 DOI: 10.1016/j.neuroimage.2009.11.006] [Citation(s) in RCA: 116] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2009] [Revised: 10/30/2009] [Accepted: 11/04/2009] [Indexed: 10/20/2022] Open
Abstract
Morphometry of brain structures based on magnetic resonance imaging (MRI) data has become an important tool in neurobiology. Recent multicenter studies in neurodegenerative diseases raised the issue of the precision of volumetric measures, and their dependence on the scanner properties and imaging protocol. A large dataset consisting of 1073 MRI examinations in 843 subjects, acquired on 90 scanners at 58 sites, is analyzed here. A comprehensive set of image quality and content measures is used to describe the influence of the scanner hardware and imaging protocol on the variability of morphometric measures. Scanners equipped with array coils show a remarkable advantage over conventional coils in terms of image quality measures. The signal- and contrast-to-noise ratio in similar systems is equal or slightly better at 1.5 T than 3.0 T, while the white/grey matter tissue contrast is generally better on high-field systems. Repeated MRI investigations on the same scanner were available in 41 subjects, on different scanners in 172 subjects. The retest reliability of repeated volumetric measures under the same conditions was found as sufficient to track changes in longitudinal examinations in individual subjects. Using different acquisition conditions in the same subject, however, the variance of volumetric measures was up to 10 times greater. Two likely factors explaining this finding are scanner-dependent geometrical inaccuracies and differences in the white/grey matter tissue contrast.
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Affiliation(s)
- Frithjof Kruggel
- Department of Biomedical Engineering, University of California, Irvine, CA 92697-2755, USA.
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Mayerhoefer ME, Szomolanyi P, Jirak D, Berg A, Materka A, Dirisamer A, Trattnig S. Effects of Magnetic Resonance Image Interpolation on the Results of Texture-Based Pattern Classification. Invest Radiol 2009; 44:405-11. [DOI: 10.1097/rli.0b013e3181a50a66] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 2009; 45:1151-61. [PMID: 19344687 DOI: 10.1016/j.neuroimage.2009.01.011] [Citation(s) in RCA: 210] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2008] [Revised: 12/03/2008] [Accepted: 01/12/2009] [Indexed: 12/24/2022] Open
Abstract
A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.
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Mayerhoefer ME, Szomolanyi P, Jirak D, Materka A, Trattnig S. Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: An application-oriented study. Med Phys 2009; 36:1236-43. [DOI: 10.1118/1.3081408] [Citation(s) in RCA: 148] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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