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This contribution presents a deep learning method for the segmentation ofprostate zones in MRI images based on U-Net using additive and feature pyramidattention modules, which can improve the workflow of prostate cancer detectionand diagnosis. The proposed model is compared to seven different U-Net-basedarchitectures. The automatic segmentation performance of each model of thecentral zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor wereevaluated using Dice Score (DSC), and the Intersection over Union (IoU)metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of76.9% in the test set, outperforming most of the studied models in this workexcept from R2U-Net and attention R2U-Net architectures.

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation