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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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Fiford CM, Sudre CH, Pemberton H, Walsh P, Manning E, Malone IB, Nicholas J, Bouvy WH, Carmichael OT, Biessels GJ, Cardoso MJ, Barnes J. Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change. Neuroinformatics 2020; 18:429-449. [PMID: 32062817 PMCID: PMC7338814 DOI: 10.1007/s12021-019-09439-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.
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Affiliation(s)
- Cassidy M. Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Hugh Pemberton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Emily Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Willem H Bouvy
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - M. Jorge Cardoso
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
- Pennington Biomedical Research Center, Baton Rouge, LA USA
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Liu L, Chen S, Zhu X, Zhao XM, Wu FX, Wang J. Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.050] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Wu D, Albert M, Soldan A, Pettigrew C, Oishi K, Tomogane Y, Ye C, Ma T, Miller MI, Mori S. Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities. NEUROIMAGE-CLINICAL 2019; 22:101772. [PMID: 30927606 PMCID: PMC6444296 DOI: 10.1016/j.nicl.2019.101772] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 02/05/2019] [Accepted: 03/10/2019] [Indexed: 02/07/2023]
Abstract
The extent and spatial location of white matter hyperintensities (WMH) on brain MRI may be relevant to the development of cognitive decline in older persons. Here, we introduce a new method, known as the Multi-atlas based Detection and Localization (MADL), to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data. This method simultaneously parcellates the whole brain into 143 structures and labels hyperintense areas within each WM structure. First, a multi-atlas library was established with FLAIR data of normal elderly brains; and then a multi-atlas fusion algorithm was developed by which voxels with locally abnormal intensities were detected as WMH. At the same time, brain segmentation maps were generated from the multi-atlas fusion process to determine the anatomical location of WMH. Areas identified using the MADL method agreed well with manual delineation, with an interclass correlation of 0.97 and similarity index (SI) between 0.55 and 0.72, depending on the total WMH load. Performance was compared to other state-of-the-art WMH detection methods, such as BIANCA and LST. MADL-based analyses of WMH in an older population revealed a significant association between age and WMH load in deep WM but not subcortical WM. The findings also suggested increased WMH load in selective brain regions in subjects with mild cognitive impairment compared to controls, including the inferior deep WM and occipital subcortical WM. The proposed MADL approach may facilitate location-dependent characterization of WMH in older individuals with memory impairment. We proposed a multi-atlas based method for simultaneous detection and location of WMH on FLAIR images. The method generates whole-brain segmentation for location-dependent WMH analysis. The method showed reasonably high detection accuracy in comparison with other methods. Results revealed a selective association between deep brain WMH and subject age. Results suggested increased WMH in the inferior white matter in MCI patients.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yusuke Tomogane
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chenfei Ye
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ting Ma
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael I Miller
- Department of Biomedicine Engineering, Johns Hopkins University, Baltimore, MD, USA; Center of Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Griffanti L, Zamboni G, Khan A, Li L, Bonifacio G, Sundaresan V, Schulz UG, Kuker W, Battaglini M, Rothwell PM, Jenkinson M. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. Neuroimage 2016; 141:191-205. [PMID: 27402600 PMCID: PMC5035138 DOI: 10.1016/j.neuroimage.2016.07.018] [Citation(s) in RCA: 251] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 07/06/2016] [Accepted: 07/07/2016] [Indexed: 12/21/2022] Open
Abstract
Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies.
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Affiliation(s)
- Ludovica Griffanti
- Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Giovanna Zamboni
- Centre for the Functional MRI of the Brain (FMRIB), 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
| | - Aamira Khan
- Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Linxin Li
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Guendalina Bonifacio
- Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Vaanathi Sundaresan
- Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ursula G Schulz
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Wilhelm Kuker
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Peter M Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark Jenkinson
- Centre for the Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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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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7
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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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Liu CY, Iglesias JE, Tu Z. Deformable templates guided discriminative models for robust 3D brain MRI segmentation. Neuroinformatics 2013; 11:447-68. [PMID: 23836390 PMCID: PMC5966025 DOI: 10.1007/s12021-013-9190-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.
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Affiliation(s)
- Cheng-Yi Liu
- Laboratory of Neuro Imaging Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225, 90095, Los Angeles, CA, USA,
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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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Khademi A, Venetsanopoulos A, Moody AR. Robust white matter lesion segmentation in FLAIR MRI. IEEE Trans Biomed Eng 2011; 59:860-71. [PMID: 22203699 DOI: 10.1109/tbme.2011.2181167] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.
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Affiliation(s)
- April Khademi
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.
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Pham TD, Berger K. Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models. ACTA ACUST UNITED AC 2010; 15:242-50. [PMID: 20889435 DOI: 10.1109/titb.2010.2081996] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.
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Affiliation(s)
- Tuan D Pham
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.
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