基于深度学习的MRI脑卒中病灶分割方法综述

余唯一; 陈涛; 张军平; 单洪明

doi:10.11959/j.issn.2096-6652.202328

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基于深度学习的MRI脑卒中病灶分割方法综述

综述与展望 | 更新时间：2024-04-04

- 基于深度学习的MRI脑卒中病灶分割方法综述
- A survey of deep learning-based MRI stroke lesion segmentation methods
- 智能科学与技术学报 2023年5卷第3期页码：293-312
- 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：
  
  [ "余唯一（1998- ），女，复旦大学类脑智能科学与技术研究院硕士生，主要研究方向为深度学习和医学图像处理" ]
  [ "陈涛（1999- ），男，复旦大学类脑智能科学与技术研究院博士生，主要研究方向为深度学习和医学图像处理" ]
  [ "张军平（1970- ），男，复旦大学计算机科学技术学院教授、博士生导师，主要研究方向为机器学习、智能交通、生物认证与图像识别" ]
  [ "单洪明（1990- ），男，复旦大学类脑智能科学与技术研究院青年研究员、博士生导师，主要研究方向为机器学习和医学影像" ]
- 基金信息：
  
  国家自然科学基金项目;The National Natural Science Foundation of China(62101136);国家自然科学基金项目;The National Natural Science Foundation of China(62176059);上海市自然科学基金项目;Natural Science Foundation of Shanghai(21ZR1403600);上海市科技计划项目;Shanghai Municipal Science and Technology Project(20JC1419500)
- DOI：10.11959/j.issn.2096-6652.202328
  中图分类号： TP39
- 修回：2023-07-12，
  
  网络首发：2023-09，
  
  纸质出版：2023-09-15
- 稿件说明：
移动端阅览
余唯一, 陈涛, 张军平, 等. 基于深度学习的MRI脑卒中病灶分割方法综述[J]. 智能科学与技术学报, 2023,5(3):293-312.

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.
余唯一, 陈涛, 张军平, 等. 基于深度学习的MRI脑卒中病灶分割方法综述[J]. 智能科学与技术学报, 2023,5(3):293-312. DOI： 10.11959/j.issn.2096-6652.202328.

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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