Impact of Label-Set on the Performance of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Prostate segmentation is an essential step in computer-aided diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the impact of ground truth (manual segmentation) selection. In this work, we investigated these effects and concluded that selecting different segmentation labels for the prostate gland and zones has a measurable impact on the segmentation model performance.