摘要
脑卒中MRI影像由于病灶区域小和正常组织边界模糊的特点导致分割难度大。为此提出一种优化的编解码结构网络。为使网络提取更加丰富的上下文信息,提出了双注意力卷积融合编码模块,在编码端收缩路径实现二维卷积和三维卷积的融合,并且从空间和通道2个维度建立特征的全局相关性。此外,提出残差-注意力门混合解码模块,更好地融合低层次和高层次特征,关注目标区域,从而提高小病灶边缘的分割细腻度。通过在开源数据集ATLAS的实验结果表明,该算法DSC指标达到了0.62,与UNet,D-UNet,3D-UNet以及attention-UNet等模型相比,有效提高了分割性能。
Stroke MRI images are difficult to be segmented due to small lesion areas and blurred boundaries between lesions and healthy tissues.Therefore,this paper proposes an optimized encoder-decoder structure network.Firstly,in order to extract richer contextual information for the network,a dual-attention convolution fusion coding module is established,which combines 2D convolution with 3D convolution on the contraction path of the encoding end,and builds global correlation of the features from both space and channel dimensions.In addition,a residual-attention gate hybrid decoding module is proposed to better fuse low-level and high-level features and focus on the target area,thereby improving the segmentation fineness of the edges of the small lesions.The experimental results in the open source dataset anatomical tracings of lesions after stroke(ATLAS)show that the DSC index of the algorithm reaches 0.62.Compared with models such as UNet,D-UNet,3D-UNet and attention-UNet,it demonstrates a much improved segmentation performance.
作者
张岩
李凤莲
张雪英
王夙喆
章洪涛
ZHANG Yan;LI Fenglian;ZHANG Xueying;WANG Suzhe;ZHANG Hongtao(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2023年第5期185-193,共9页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(62171307)
山西省自然科学基金面上项目(202103021224113)
太原理工大学校精品课程项目(2021KC10)。
关键词
脑卒中
UNet
卷积融合
注意力
stroke
UNet
convolution fusion
attention mechanism
