Using Channel Concatenation and Lightweight Atrous Convolution in U-net for Accurate Rectal Cancer Segmentation

Accurate segmentation of rectal cancer from CT images of rectal cancer is the basis of rectal cancer diagnosis. In this article, we propose a new segmentation method based on fully convolutional neural networks. The encoding block consists of a channel concatenation block and a residual block (CesBlock) and a lightweight atrous convolution block (LacBlock) for skip connection. By combining dice loss and cross entropy loss as the new loss function, the obtained model can realize automatic and accurate segmentation of CT images of rectal cancer. The contraction path of the symmetrical convolutional neural network ensures enough feature extraction, and the expansion path ensures the recovery of pixels. In the experiments, the recognized evaluation indicators are utilized to verify the proposed method, which has an improvement of 2.7% on the dice similarity coefficient (DSC).