Aiming at the problems of low segmentation accuracy and over-segmentation in the current automatic segmentation of prostate tissue regions on magnetic resonance images, a U-Net segmentation algorithm combining dense connections and Inception modules was proposed. Firstly, the contrast-limited adaptive histogram equalization method was used to process the prostate image to enhance the detectability of the information. In addition, the algorithm introduces the idea of ​​dense connection into the U-Net model, improves the connection method of the original encoder and decoder, and realizes the fusion and dissemination of multi-scale semantic information. Meanwhile, the Inception module driven by atrous convolution is used to replace the original concatenated convolution operation to increase the width of the network and enhance the feature extraction and expression capabilities for objects of different sizes. Finally, for the over-segmentation problem of non-organized objects, a corrector with classification-guided function is designed to reduce false positive predictions. By testing on the public dataset of NCI-ISBI 2013 Challenge, using Dice similarity coefficient, accuracy rate and false positive rate as evaluation criteria, the mean values ​​can reach 86.12%, 97.96% and 1.11%, respectively. The experimental results show that this algorithm has better segmentation effect than other segmentation algorithms.