MC-Net: multi-scale Swin transformer and complementary self-attention fusion network for pancreas segmentation

Abstract

The pancreas is located deep in the abdominal cavity, and its structure and adjacent relationship are complex. It is very difficult to treat it accurately. In order to solve the problem of automatic segmentation of pancreatic tissue in CT images, we apply the multi-scale idea of convolution neural network to Transformer, and propose a Multi-Scale Swin Transformer and Complementary Self-Attention Fusion Network for Pancreas Segmentation. Specifically, the multi-scale Swin Transformer module constructs different receptive fields through different window sizes to obtain multi-scale information; the different features of the encoder and decoder are effectively fused through a complementary self-attention fusion module. By comparing experimental evaluations on the NIH-TCIA dataset, our method improves Dice, sensitivity, and IOU by 3.9%, 6.4%, and 5.3% respectively compared to the baseline, which outperforms current state-of-the-art medical image segmentation methods.