FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
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Abstract (arXiv)
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.
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References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
- 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
- 2017Deeply-Supervised CNN for Prostate Segmentationcited by 30
- 2019Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attentioncited by 10
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