Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning

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dc.citation.articleNumber17157
dc.citation.journalTitleScientific Reports
dc.citation.volumeNumber14
dc.contributor.authorMusall, Benjamin C.
dc.contributor.authorGabr, Refaat E.
dc.contributor.authorYang, Yanyu
dc.contributor.authorKamali, Arash
dc.contributor.authorLincoln, John A.
dc.contributor.authorJacobs, Michael A.
dc.contributor.authorLy, Vi
dc.contributor.authorLuo, Xi
dc.contributor.authorWolinsky, Jerry S.
dc.contributor.authorNarayana, Ponnada A.
dc.contributor.authorHasan, Khader M.
dc.date.accessioned2024-08-09T16:25:26Z
dc.date.available2024-08-09T16:25:26Z
dc.date.issued2024
dc.description.abstractIn 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.
dc.identifier.citationMusall, B. C., Gabr, R. E., Yang, Y., Kamali, A., Lincoln, J. A., Jacobs, M. A., Ly, V., Luo, X., Wolinsky, J. S., Narayana, P. A., & Hasan, K. M. (2024). Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning. Scientific Reports, 14(1), 17157. https://doi.org/10.1038/s41598-024-67722-2
dc.identifier.digitals41598-024-67722-2
dc.identifier.doihttps://doi.org/10.1038/s41598-024-67722-2
dc.identifier.urihttps://hdl.handle.net/1911/117646
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDetection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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