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

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dc.citation.articleNumber17157en_US
dc.citation.journalTitleScientific Reportsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorMusall, Benjamin C.en_US
dc.contributor.authorGabr, Refaat E.en_US
dc.contributor.authorYang, Yanyuen_US
dc.contributor.authorKamali, Arashen_US
dc.contributor.authorLincoln, John A.en_US
dc.contributor.authorJacobs, Michael A.en_US
dc.contributor.authorLy, Vien_US
dc.contributor.authorLuo, Xien_US
dc.contributor.authorWolinsky, Jerry S.en_US
dc.contributor.authorNarayana, Ponnada A.en_US
dc.contributor.authorHasan, Khader M.en_US
dc.date.accessioned2024-08-09T16:25:26Zen_US
dc.date.available2024-08-09T16:25:26Zen_US
dc.date.issued2024en_US
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.en_US
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/s41598-024-67722-2en_US
dc.identifier.digitals41598-024-67722-2en_US
dc.identifier.doihttps://doi.org/10.1038/s41598-024-67722-2en_US
dc.identifier.urihttps://hdl.handle.net/1911/117646en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDetection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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