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

Journal: Frontiers in neurology
Published Date: May 27, 2025

Abstract

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).

Authors

  • Huiyu Zhao
    The State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Miaoyi Zhang
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Weijun Tang
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China.
  • Luyuan Jin
    The State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Jie Tang
    Department of Computer Science and Technology, Tsinghua University, Beijing, China jietang@tsinghua.edu.cn.
  • Langfeng Shi
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Xiao Deng
    The State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Jianhui Fu
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Weiwen Zou

Keywords

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