FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This work addresses prostate cancer detection and diagnosis for medical imaging, but it is incremental as it builds on existing U-Net architectures with attention mechanisms.
The paper tackled prostate cancer segmentation in MRI images by extending U-Net with attention modules, achieving a mean Dice Score of 84.15% and IoU of 76.9%, outperforming most compared models except R2U-Net variants.
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.