Automatic Segmentation of Diffusely Abnormal White Matter in Multiple Sclerosis using Fuzzy C-means Clustering

Motivation: Diffusely abnormal white matter (DAWM) occurs in ~25% of multiple sclerosis patients. Currently there is no consensus on how to best automatically segment DAWM which is difficult to identify and quantify. Goal(s): Use fuzzy c-means clustering to segment DAWM. Approach: A data set from DAWM+ patients (n=40) was used to train a clustering algorithm on proton-density (PDw) and T2-weighted (T2w) images. This algorithm was tested on 9 patients visually identified as DAWM+ and 51 patients visually identified as DAWM-. Results: Clustering segmented PDw/T2w hyperintensities in DAWM+ test subjects. Patients visually identified as DAWM- also had regions segmented as DAWM. Impact: Unsupervised clustering could provide an unbiased approach to automatically segment diffusely abnormal white matter (DAWM) in multiple sclerosis patients in vivo. In this study, the subtle hyperintensities associated with DAWM were segmented on proton density and T2-weighted images.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3581
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Automatic Segmentation of Diffusely Abnormal White Matter in Multiple Sclerosis using Fuzzy C-means Clustering

Cornelia Laule, Robert Carruthers, Shannon Kolind, Anthony Traboulsee et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Brain Tumor Detection and Classification
article

Automatic Segmentation of Diffusely Abnormal White Matter in Multiple Sclerosis using Fuzzy C-means Clustering

Cornelia Laule, Robert Carruthers, Shannon Kolind, Anthony Traboulsee, Tigris Joseph, Peng Sun, Sharada Balaji, Irene Vavasour, Guojun Zhao, David K.B. Li, Roger C. Tam, Alice Schabas, Virginia Devonshire, George R. Moore, Ana‐Luiza Sayao
article en

Abstract

Motivation: Diffusely abnormal white matter (DAWM) occurs in ~25% of multiple sclerosis patients. Currently there is no consensus on how to best automatically segment DAWM which is difficult to identify and quantify. Goal(s): Use fuzzy c-means clustering to segment DAWM. Approach: A data set from DAWM+ patients (n=40) was used to train a clustering algorithm on proton-density (PDw) and T2-weighted (T2w) images. This algorithm was tested on 9 patients visually identified as DAWM+ and 51 patients visually identified as DAWM-. Results: Clustering segmented PDw/T2w hyperintensities in DAWM+ test subjects. Patients visually identified as DAWM- also had regions segmented as DAWM. Impact: Unsupervised clustering could provide an unbiased approach to automatically segment diffusely abnormal white matter (DAWM) in multiple sclerosis patients in vivo. In this study, the subtle hyperintensities associated with DAWM were segmented on proton density and T2-weighted images.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 68%
Brain Tumor Detection and Classification
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