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

Journal: Scientific reports
PMID: 39060426

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.

Authors

  • Benjamin C Musall
    Department of Diagnostic and Interventional Imaging, University of Texas McGovern Medical School, 6431 Fannin St., MSE 168, Houston, TX, 77030, USA.
  • Refaat E Gabr
    Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, USA.
  • Yanyu Yang
    Department of Biostatistics and Data Science, University of Texas School of Public Health, Houston, TX, USA.
  • Arash Kamali
    Department of Diagnostic and Interventional Imaging, University of Texas McGovern Medical School, 6431 Fannin St., MSE 168, Houston, TX, 77030, USA.
  • John A Lincoln
    Department of Neurology, University of Texas McGovern Medical School, Houston, TX, USA.
  • Michael A Jacobs
    The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Vi Ly
    Computer Science and Engineering Technology, University of Houston-Downtown, Houston, TX 77002, USA.
  • Xi Luo
    Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Jerry S Wolinsky
    Department of Neurology, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, USA.
  • Ponnada A Narayana
    Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, USA.
  • Khader M Hasan
    Department of Diagnostic & Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX, USA.

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