Detection of Diffusely Abnormal White Matter in Multiple Sclerosis on Multiparametric Brain MRI Using Semi-Supervised Deep Learning

Authors

Benjamin C Musall
Refaat E Gabr
Yanyu Yang
Arash Kamali
John A Lincoln
Michael A Jacobs
Vi Ly
Xi Luo
Jerry S Wolinsky
Ponnada A Narayana
Khader M Hasan

Publication Date

7-26-2024

Journal

Scientific Reports

Abstract

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS.

Keywords

Humans, Deep Learning, White Matter, Multiple Sclerosis, Male, Multiparametric Magnetic Resonance Imaging, Female, Brain, Adult, Middle Aged, Supervised Machine Learning, Magnetic Resonance Imaging

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Supplementary Materials

PMID: 39060426