The Abdominal Multi-organ Image Segmentation Method Based on SF-TransUNet
GUO Yuting
YU Li
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.
Keywords：SF-TransUNetmulti-organ image segmentationsmall organ image segmentationtexture information enhancementshuffle attentionconnected component analysis
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 94-100 )