现代电子技术2024，Vol.47Issue(23)：29-35,7.DOI:10.16652/j.issn.1004-373x.2024.23.005
基于短距离跳跃连接的U2-Net+医学图像语义分割
U2-Net+medical image semantic segmentation based on short-range jump connection
摘要
Abstract
Medical image segmentation is one of the necessary prerequisites to guarantee the development of an intelligent medical system.Because the jump connection of the original U2-Net+network only focuses on the features extracted at the same resolution,an intermediate layer is added in the design by taking the FR-UNet network as reference.The contextual information from the deeper layer is received by the intermediate layer to integrate with the high-resolution features extracted from the shallower layer.In the down-sampling of the intermediate layer,a convolutional pyramid with asymmetric atrous space is used to increase the attention to edge information during network model training.A threshold value enhancement module is added at the end of the structure to strengthen the identification and segmentation of the edges with fine features.It is also added to the up-sampling to help the network extract multi-scale features better and increase contextual semantic associations.A combined loss function is designed to supervise network optimization according to the imbalance between the positive and negative samples and the different levels of difficulties.The experimental results show that the proposed algorithm improves the F1-score by 1.8%and 4.2%on the datasets of DRIVE and STARE+CHASE_DB1,respectively,and improves the DSC score by 2.3%on the dataset ISIC2018.Visualization of the segmentation results shows that the present network can fully extract more accurate edge information and fine feature information to improve the semantic segmentation in the case of smaller samples,so the proposed algorithm has a better performance on the task of semantic segmentation of medical images.关键词
医学图像/语义分割/跳跃连接/非对称空洞空间卷积金字塔/智慧医疗/FR-UNet网络Key words
medical image/semantic segmentation/jump connection/convolutional pyramid with asymmetric dilated space/intelligent medical care/FR-UNet network分类
电子信息工程引用本文复制引用
王清华,孙水发,吴义熔..基于短距离跳跃连接的U2-Net+医学图像语义分割[J].现代电子技术,2024,47(23):29-35,7.基金项目
国家社会科学基金项目:基于数据语义化的电子病历数据质量研究基金(20BTQ066) （20BTQ066）
