FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for
  Prostate Cancer Segmentation
Pablo Cesar Quihui-Rubio Daniel Flores-Araiza Miguel Gonzalez-Mendoza Christian Mata Gilberto Ochoa-Ruiz
This contribution presents a deep learning method for the segmentation of
prostate zones in MRI images based on U-Net using additive and feature pyramid
attention modules, which can improve the workflow of prostate cancer detection
and diagnosis. The proposed model is compared to seven different U-Net-based
architectures. The automatic segmentation performance of each model of the
central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were
evaluated using Dice Score (DSC), and the Intersection over Union (IoU)
metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of
76.9% in the test set, outperforming most of the studied models in this work
except from R2U-Net and attention R2U-Net architectures.