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.

翻译：本研究聚焦于比较基于MRI图像的前列腺分割中深度学习方法的性能及其不确定性量化，旨在改善前列腺癌检测与诊断的工作流程。我们评估了七种基于U-Net架构并集成蒙特卡洛dropout的模型，用于中央区、外周区、移行区及肿瘤的自动分割与不确定性估计。研究表现最佳的模型为Attention R2U-Net，其在各区域分割中实现了平均交并比（IoU）达76.3%、Dice相似系数（DSC）达85%的优异性能。此外，与其他模型相比，Attention R2U-Net在移行区及肿瘤边界区域展现出最低的不确定性值。