  Deep learning algorithms are preferable for rectal tumor segmentation.
However, it is still a challenge task to accurately segment and identify the
locations and sizes of rectal tumors by using deep learning methods. To
increase the capability of extracting enough feature information for rectal
tumor 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 path
and two expansive paths (nam new network as DPUNet), which can help extract
more feature information from CT images; 2) employ the criss-cross
self-attention module into DPUNet, meanwhile, replace the original calculation
method of correlation operation with covariance operation, which can further
enhances the characterization ability of DPUNet and improves the segmentation
accuracy of rectal tumors. Experiments illustrate that compared with the
current state-of-the-art results, CSA-DPUNet brings 15.31%, 7.2%, 11.8%, and
9.5% improvement in Dice coefficient, P, R, F1, respectively, which
demonstrates that our proposed CSA-DPUNet is effective for rectal tumor
segmentation.
