Assessing the Performance of Deep Learning-Based Models for Prostate Cancer Segmentation Using Uncertainty Scores

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 % ± 0.003 and Dice Similarity Coefficient (DSC) of 85 % ± 0.003 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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessing the Performance of Deep Learning-Based Models for Prostate Cancer Segmentation Using Uncertainty Scores Chapter in Scopus