
Impact of uncertainty quantification through conformal prediction on volume assessment from DL-based MRI prostate segmentation
This study aimed to evaluate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm using conformal prediction (CP) and its impact on prostate volume (PV) calculation in patients at risk of prostate cancer (PC). The study involved 377 patients’ 3-Tesla T2-weighted scans. By applying CP at an 85% confidence level, unreliable pixel segmentations of the DL model were flagged,

