A new approach for automatic segmentation of prostate and its lesion regions on the magnetic resonance imaging

Motivation: In recent years,many neural network models based on prostate lesion segmentation in magnetic resonance images have different stability and diagnostic efficiency. Goal(s): we want to get an automatic segmentation model with high performance for the prostate and its lesion region. Approach: Our Network DCNN is inspired by the U-Net model with the encoding-decoding path as the backbone,importing dense block,attention mechanism techniques,and group norm-Atrous Spatial Pyramidal Pooling,these could be broadly used to improve the capability of CNN. Results: Compared to the state-of-the-art models,FCN,U-Net,U-Net++,and ResUNet.The segmentation performance of DCNN for prostate lesions On the MR DWI image swas better than the other models. Impact: The DCNN model with dense block, convolution block attention module, and group norm-Atrous Spatial Pyramid Pooling performed well in the segmentation of the prostate and its lesion regions. which supports its potential to assist prostate disease diagnosis in clinical medicine.