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Deep learning algorithms are preferable for rectal tumor segmentation.However, it is still a challenge task to accurately segment and identify thelocations and sizes of rectal tumors by using deep learning methods. Toincrease the capability of extracting enough feature information for rectaltumor segmentation, we propose a Covariance Self-Attention Dual Path UNet(CSA-DPUNet). The proposed network mainly includes two improvements on UNet: 1)modify UNet that has only one path structure to consist of two contracting pathand two expansive paths (nam new network as DPUNet), which can help extractmore feature information from CT images; 2) employ the criss-crossself-attention module into DPUNet, meanwhile, replace the original calculationmethod of correlation operation with covariance operation, which can furtherenhances the characterization ability of DPUNet and improves the segmentationaccuracy of rectal tumors. Experiments illustrate that compared with thecurrent state-of-the-art results, CSA-DPUNet brings 15.31%, 7.2%, 11.8%, and9.5% improvement in Dice coefficient, P, R, F1, respectively, whichdemonstrates that our proposed CSA-DPUNet is effective for rectal tumorsegmentation.

Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation