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

Shunan Wang; Jiancong Fan

doi:10.1117/12.2680916

8 June 2023 MC-Net: multi-scale Swin transformer and complementary self-attention fusion network for pancreas segmentation

Shunan Wang, Jiancong Fan

Author Affiliations +

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072T (2023) https://doi.org/10.1117/12.2680916
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

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.

Citation Download Citation

Shunan Wang and Jiancong Fan "MC-Net: multi-scale Swin transformer and complementary self-attention fusion network for pancreas segmentation", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072T (8 June 2023); https://doi.org/10.1117/12.2680916

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KEYWORDS

Transformers

Image segmentation

Pancreas

Convolution

Medical imaging

Windows

Neural networks

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