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

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

ObjectiveExisting 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...

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Bibliographic Details
Main Authors: Huiyu Zhao, Miaoyi Zhang, Weijun Tang, Luyuan Jin, Jie Tang, Langfeng Shi, Xiao Deng, Jianhui Fu, Weiwen Zou
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Neurology
Subjects:
cerebral small vessel diseases
deep learning
global burden of disease
quantitative evaluation
multisequence MRI
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2025.1540923/full
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https://www.frontiersin.org/articles/10.3389/fneur.2025.1540923/full

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