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

TL;DR

This paper compares various deep learning models, specifically U-Net variants with Monte-Carlo dropout, for prostate MRI segmentation and uncertainty estimation, highlighting the superior performance of Attention R2U-Net in accuracy and uncertainty reduction.

Contribution

It introduces a comprehensive evaluation of seven U-Net-based architectures with uncertainty estimation for prostate MRI segmentation, identifying the best model and its performance metrics.

Findings

Attention R2U-Net achieved 76.3% IoU and 85% DSC.

It exhibited the lowest uncertainty in boundary regions.

The study enhances prostate cancer detection workflows.

Abstract

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.