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

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。

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.