  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.
