Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI

作者：Huiyu Zhao, Miaoyi Zhang, Weijun Tang, Luyuan Jin, Jie Tang, Langfeng Shi, Xiao Deng, Jianhui Fu, Weiwen Zou · 发表于：Frontiers in Neurology · 年份：2025 · DOI：10.3389/fneur.2025.1540923 · 被引用次数：2 · 研究领域：Intracerebral and Subarachnoid Hemorrhage Research、Cerebrospinal fluid and hydrocephalus、Dementia and Cognitive Impairment Research

Objective Existing visual scoring systems for cerebral small vessel disease (CSVD) cannot assess the global lesion load accurately and quantitatively. We aimed to develop an automated segmentation method based on deep learning (DL) to quantify the typical neuroimaging markers of CSVD on multisequence magnetic resonance imaging (MRI). Materials and methods MRI scans from internal (July 2018 to July 2022) and external (November 2012 to January 2015) datasets were analyzed. A DL-based segmentation method was developed to evaluate the quantitative volumes of white matter hyperintensity (WMH), cerebral microbleeds (CMBs), lacunes, and enlarged perivascular spaces (EPVSs) according to the segmentation results. Dice and other quantitative metrics were used to access the DL segmentation results. Pearson correlation coefficients were used for correlation analysis, and the differences in marker volumes among different visual scores were assessed via analysis of variance (ANOVA). Finally, a quantitative Z score was calculated to represent CSVD-related brain burden. Results A total of 105 internal patients (64.8 ± 7.4 years, 70 males) and 58 external patients (68.2 ± 6.8 years, 29 males) were evaluated. The Dice values for WMH, CMBs, lacunes, and EPVSs in the internal dataset were 0.85, 0.74, 0.76, and 0.75, respectively. The positive correlation between the DL and the manual approach results was excellent (overall Pearson correlation = 0.968, 0.978, 0.948, and 0.947, respectively). The ...