湖北民族大学学报（自然科学版）2025，Vol.43Issue(1)：94-100,7.DOI:10.13501/j.cnki.42-1908/n.2025.03.007
基于SF-TransUNet的腹部多器官图像分割方法
The Abdominal Multi-organ Image Segmentation Method Based on SF-TransUNet
摘要
Abstract
To address the challenge of low image segmentation accuracy for small organs in abdominal multi-organ image segmentation,an improved model based on the Transformer U-shaped network(TransUNet)called segmentation fusion TransUNet(SF-TransUNet)was proposed to enhance the image segmentation accuracy of small organs.The improved position attention module(PAM)for texture information enhancement was introduced into the skip connections of TransUNet,and a high-and low-level feature fusion shuffle attention(SA)module was added in the decoder to improve the capture ability of fine details for small organs image.Additionally,connected component analysis(CCA)module was designed as a post-processing step to effectively enhance edge segmentation capabilities.The experiments on the Synapse dataset validated the performance of SF-TransUNet model,with results showing that the average Dice similarity coefficient(DSC)had an increase of 2.69 percentage points compared to the TransUNet model,and the 95%Hausdorff distance(HD95)had a decrease of 17.26mm.For small organs,image segmentation accuracy for the gallbladder,right kidney and pancreas improved by 9.22,4.76 and 4.49 percentage points,respectively.The findings demonstrated that SF-TransUNet model not only enhanced the overall accuracy of abdominal multi-organ image segmentation significantly but also exhibited superior feature representation and detail retention for small organ image segmentation.关键词
SF-TransUNet/多器官图像分割/小器官图像分割/纹理信息增强/混洗注意力/连通域分析Key words
SF-TransUNet/multi-organ image segmentation/small organ image segmentation/texture information enhancement/shuffle attention/connected component analysis分类
信息技术与安全科学引用本文复制引用
郭雨婷,于瓅..基于SF-TransUNet的腹部多器官图像分割方法[J].湖北民族大学学报（自然科学版）,2025,43(1):94-100,7.基金项目
安徽省重点研究与开发计划项目(202104d07020010). （202104d07020010）
