A survey of deep learning-based MRI stroke lesion segmentation methods

Weiyi YU; Tao CHEN; Junping ZHANG; Hongming SHAN

doi:10.11959/j.issn.2096-6652.202328

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A survey of deep learning-based MRI stroke lesion segmentation methods

Surveys and Prospectives | 更新时间：2024-04-04

- A survey of deep learning-based MRI stroke lesion segmentation methods
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 3, Pages: 293-312(2023)
- 作者机构：
  
  1. 复旦大学类脑智能科学与技术研究院，上海 200433
  2. 复旦大学计算机科学技术学院，上海 200433
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202328
  CLC： TP39
- Revised：2023-07-12，
  
  Online First：2023-09，
  
  Published：15 September 2023
- 稿件说明：
移动端阅览
Weiyi YU, Tao CHEN, Junping ZHANG, et al. A survey of deep learning-based MRI stroke lesion segmentation methods[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 293-312.
DOI：

Weiyi YU, Tao CHEN, Junping ZHANG, et al. A survey of deep learning-based MRI stroke lesion segmentation methods[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 293-312. DOI： 10.11959/j.issn.2096-6652.202328.

摘要

脑卒中病灶自动分割方法成为近几年的研究热点。为了全面研究用于MRI脑卒中病灶分割的深度学习方法的现状，针对脑卒中治疗的临床问题，进一步阐述了基于深度学习的病灶分割的研究背景及其挑战性，并介绍脑卒中病灶分割的常用公共数据集（ISLES和ATLAS）。然后，重点阐述了基于深度学习的脑卒中病灶分割方法的创新与进展，从网络结构、训练策略、损失函数这3个角度对研究进展进行了归纳，并且对比了各种方法的优缺点。最后，讨论了该研究存在的困难和挑战以及未来的发展趋势。

Abstract

Automatic stroke lesion segmentation has become a research hotspot in recent years.In order to comprehensively review current progress of deep learning-based MRI stroke lesion segmentation methods

start with the clinical problems of stroke treatment

we further elaborate the research background and challenges of deep learning-based lesion segmentation

and introduce common public datasets (ISLES and ATLAS) for stroke lesion segmentation.Then

we focus on the innovation and progress of deep learning-based stroke lesion segmentation methods

and summarize the research progress from three perspectives: network structure

training strategy

and loss function

and compare the advantages and disadvantages of various methods.Finally

we discusse the difficulties and challenges in this research and its future development trend.

关键词

Keywords

references

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