After prostate magnetic resonance (MR) image slices,it is found that some images do not have effective edge information,which makes it impossible to clearly locate the edge position,and thus cannot segment the prostate.At the same time,the traditional convolutional network requires a large amount of parameters and takes up too much storage space of the model.This paper proposes a method to segment the prostate using U-Net that combines multi-scale dilated separable convolution and channel attention.First,slice 50 three-dimensional (3D) prostate samples and perform contrast enhancement on the sliced images.Subsequently,the processed data is input into the residual U-Net,and the multi-scale dilated convolution and channel attention are used as the encoding-decoding unit to extract the feature information.Finally,the Dice coefficient and Hausdorff distance (HD) are used to evaluate the segmentation results.The experiment was verified on the PROMISE12 challenge dataset,and the final Dice coefficient and HD were 88.13% and 14.17 mm,respectively,and the parameter amount and storage space were reduced by 57%.The results show that this method can not only segment the prostate area without effective edges to improve its segmentation accuracy,but also effectively reduce the parameter amount and storage space,and can be applied to medical images with blurred edges.