基于深度学习的MRI脑卒中病灶分割方法综述

doi:10.11959/j.issn.2096-6652.202328

Abstract

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.

Key words: stroke, medical image segmentation, computer vision, deep learning, neural network

CLC Number:

TP39

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.

Figures/Tables 19

年份	方法	特点	是否公开代码
2017年	EDD Net and MUSCLE Net^[34]	提出双阶段训练策略，先获得初步分割结果，再进行细化	否
2018年	uResNet^[35]	利用残差连接优化卷积模型	否
2018年	Res-FCN^[36]	利用残差连接优化卷积模型	否
2019年	X-Net^[37]	提出特征相似度模块以捕获上下文信息	是
2019年	D-UNet^[38]	设计维度转换模块以融合3D和2D图像特征	否
2019年	Multi-path 2.5D CNN^[39]	在轴状面、矢状面和冠状面方向上做2D分割后再融合，并进行3D后处理	是
2019年	MSDF-Net^[40]	将空洞空间金字塔池化用于医学影像分割框架	否
2019年	CLCI-Net^[41]	1.在UNet结构的编码器和解码器之间嵌入了空洞空间金字塔池化；	是
		2.提出跨层特征融合策略以整合各层特征
2020年	MI-UNet^[42]	利用大变形微分同胚度量映射算法^[43]获得组织解剖分割，以作为额外输入	否
2020年	A 3D+2D CNN approach^[44]	先用3D U-net获取病灶概率图，再用2D UNet获得最终预测结果	否
2020年	3D residual CNN^[45]	采用“放大＆缩小训练策略”	否
2020年	MUDCap3^[46]	用胶囊网络代替卷积层和池化层	否
2020年	Multi-task^[47]	图像重建和图像分割的多任务学习	否
2020年	Hybrid Dice loss^[48]	结合Dice损失与BCE损失，应对正负样本不平衡问题	否
2021年	DFENet^[49]	提出特征融合边界，引导网络进行2D-3D特征融合	否
2021年	Variational mode decomposition^[50]	利用变分模态分解做预处理	否
2021年	ME-Net^[51]	1.在UNet结构的编码器和解码器之间嵌入空洞空间金字塔池化；	否
		2.提出残差编码器，用于提取高维度特征
2021年	Priors from healthy subjects^[52]	将健康被试图像作为先验辅助分割	否
2021年	MDAN^[53]	针对大脑“伪对称”的形状特点设计专门的网络结构	否
2022年	3D UNet^[54]	3D医学影像分割	是
2022年	e-UNets^[55]	设计全局特征注意力模块，以获得全局特征	否
2022年	STHarDNet^[56]	用HarDNet^[57]模块代替卷积模块	否
2022年	AGMR-Net^[58]	1.在解码器阶段设计粗糙粒度块注意力模块，应对类内不一致问题；	否
		2.设计维度特征融合模块，应对类内不确定问题
2022年	METranse^[59]	利用4种不同尺度的编码器提取多尺度特征，再利用Transformer进行全局特征建模	否
2022年	MAPPING ^[60]	提出一种新的分割后处理策略	是
2022年	MLiRA-Ne^t[61]	设计多尺度长距离区域关注模块，在多尺度层面提取全局信息	否
2023年	CarveMix^[62]	随机组合两张医学影像作为数据增强	否
2023年	ICI loss^[63]	将Dice损失与实例级和实例中心损失结合，以减轻实例不平衡问题	是
2023年	TSRL-Net^[64]	1.设计粗糙粒度残差学习模块修正残差连接，令解码器更关注于未预测区域；2.提出一种新的目标感知损失，通过调节聚焦因子扩展聚焦样本区域	是
2023年	Fine-grained segmentation^[65]	将中风影响区域定义为常规定义病变的详细子区域，并据此定义细粒度的分割任务	否
2023年	SQMLP-net^[66]	同时进行脑卒中病变分割和TICI评级	否
		提出一种新颖的双阶段病灶分割方法，利用CNN和Transformer作为骨架网络：
2023年	W-Net^[67]	1.一阶段利用CNN的局部信息提取能力获得大致病变分割图，第二个阶段利用Transformer的强大全局上下文建模能力对边界进行细致分割；	否
		2.设计边界变形模块和边界约束模块来应对模糊边界的苛刻条件
2023年	SAN-Net^[68]	针对不同中心影像分布差异设计域适应分割算法：
		1.提出自适应实例归一化（masked adaptive instance normalization，MAIN）算法；	是
		2.利用梯度反转层迫使UNet编码器使用域分类器学习域不变表示
2023年	FAN-Net^[69]	针对不同中心影像分布差异，设计域适应分割算法，提出基于傅里叶的自适应标准化模块	否

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名称	训练样本数量/个	测试样本数量/个
ISLES 2015–SISS	28	36
ATLAS（v2.0）	655	300

数据集	受试者个数n	验证方式	方法	实验结果（Dice系数）
	229	留一域法交叉验证	SAN-Net^[68]	0.5711
	229	留一域法交叉验证	FAN-Net^[69]	0.5591
	239	六折交叉验证	W-Net^[67]	0.6176
	229	五折交叉验证	X-Net^[37]	0.4867
	54 + 45	五折交叉验证	Multi-path 2.5D CNN^[39]	0.62
	229	五折交叉验证	MI-UNet^[42]	0.5672
	229	五折交叉验证	A 3D+2D CNN approach^[44]	0.5625
	239	五折交叉验证	Hybrid Dice loss^[48]	0.5738
	229	五折交叉验证	DFENet^[49]	0.5457
	229	五折交叉验证	MDAN^[53]	0.5662
	220	五折交叉验证	MLiRA-Net^[61]	0.6119
	304	五折交叉验证	METranse^[59]	0.931
	229	80%训练、20%测试	D-UNet^[38]	0.5349
ATLAS	229	80%训练、20%测试	e-UNets^[55]	0.592
	229	80%训练、20%测试	AGMR-Net^[58]	0.594
	229	80%训练、20%测试	TSRL-Net^[64]	0.634
	239	60%训练、20%验证、20%测试	Variational mode decomposition^[50]	0.6684
	229	70%训练、30%测试	MSDF-Net^[40]	0.6875
	229	70%训练、30%测试	MUDCap3^[46]	0.67
	229	52%训练、18%验证、30%测试	CLCI-Net^[41]	0.581
	229	52%训练、18%验证、30%测试	Multi-task^[47]	0.518
	239	76%训练、11%验证、13%测试	3D residual CNN^[45]	0.64
	229	80%训练、10%验证、10%测试	ME-Net^[51]	0.6353
	220	77%训练、23%测试	Priors from healthy subjects^[52]	0.6256
	229	77%训练、23%测试	STHarDNet^[56]	0.5547
	220	77%训练、23%测试	CarveMix^[62]	0.6391
	220 + 60	80%训练、10%验证、10%测试	Fine-grained segmentation^[65]	0.5596
	655	五折交叉验证	3D UNet^[54]	0.65
ATLAS R2.0	955	655训练、300测试	MAPPING ^[60]	0.6667
	955	600训练、55验证、300测试	ICI loss^[63]	0.5817
	655	80%训练、20%测试	SQMLP-net^[66]	0.7098
	64	五折交叉验证	AGMR-Net^[58]	0.614
ISLES	64	五折交叉验证	METranse^[59]	0.793
	28	五折交叉验证	TSRL-Net^[64]	0.640

中心信息	位置	扫描仪	患者人数
Medical University General Hospital	中国天津	GE 750 Discovery	55
University of Tübingen	德国图宾根	GE Signa Excite	34
Sunnaas Rehabilitation Hospital	挪威内索登	Siemens Trio	27
NORMENT and KG Jebsen Centrefor Psychosis Research	挪威奥斯陆	Siemens Trio	12
Department of Psychology	挪威菲利普斯	Phillips Achieva	27
Child Mind Institute	美国纽约	Siemens Trio	14
Nathan S.Kline Institutefor Psychiatric Research	美国奥兰治堡	Siemens Trio	11
University of Texas Medical Branch	美国加尔维斯顿	GE 750 Discovery	35
University of Michigan	美国安娜堡	Siemens Trio	14

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

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