Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
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Abstract (arXiv)
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Net-based architectures, augmented with Monte-Carlo dropout, are evaluated for automatic segmentation of the central zone, peripheral zone, transition zone, and tumor, with uncertainty estimation. The top-performing model in this study is the Attention R2U-Net, achieving a mean Intersection over Union (IoU) of 76.3% and Dice Similarity Coefficient (DSC) of 85% for segmenting all zones. Additionally, Attention R2U-Net exhibits the lowest uncertainty values, particularly in the boundaries of the transition zone and tumor, when compared to the other models.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 9 Aug 2023.
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References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 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
- 2018Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learningcited by 19
- 2021Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Imagescited by 3
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