DMU-Net: a dual-channel multi-scale medical image segmentation model

Zhuang Yao; Ke Cheng; Yan Zhang

doi:10.1117/12.2681298

8 June 2023 DMU-Net: a dual-channel multi-scale medical image segmentation model

Zhuang Yao, Ke Cheng, Yan Zhang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270731 (2023) https://doi.org/10.1117/12.2681298
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Automatic segmentation of the liver from the patients CT is of great significance for the diagnosis of liver diseases. Because the use of bottom-up feature fusion in U-Net ignores the importance of low-level features, resulting in poor network segmentation performance, and the similarity of gray values between liver and adjacent organs and tissues, making some small detail features difficult to pay attention to. In this work, we propose a network called DMU-Net. DMU-Net replaces the original convolution module with dual-channel multi-scale convolution to obtain richer semantic information. In addition, the attention mechanism is added between the encoder and decoder to increase the feature extraction ability of the model for small targets. The results on LITS dataset show that the DICE coefficient and IOU of the proposed model get 97.46% and 93.27% separately. Compared with the classic U-Net network and MultiResUNet, the model can segment more accurate liver regions and achieve better segmentation results.

Citation Download Citation

Zhuang Yao, Ke Cheng, and Yan Zhang "DMU-Net: a dual-channel multi-scale medical image segmentation model", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270731 (8 June 2023); https://doi.org/10.1117/12.2681298

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KEYWORDS

Image segmentation

Liver

Convolution

Feature extraction

Medical imaging

Computed tomography

Data modeling

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