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This study focuses on comparing deep learning methods for the segmentationand quantification of uncertainty in prostate segmentation from MRI images. Theaim 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-performingmodel in this study is the Attention R2U-Net, achieving a mean Intersectionover Union (IoU) of 76.3% and Dice Similarity Coefficient (DSC) of 85% forsegmenting all zones. Additionally, Attention R2U-Net exhibits the lowestuncertainty values, particularly in the boundaries of the transition zone andtumor, when compared to the other models.

Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores