Organ segmentation is an essential step in computer-aided diagnosis systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about their repeatability. In this work, we investigated the intra-patient repeatability of shape features for DL-based segmentation methods of the whole prostate (WP), peripheral zone (PZ) and transition zone (TZ) on T2-weighted MRI, and compared it to the repeatability of manual segmentations. We found that the repeatability of the investigated methods is comparable to manual for most of the investigated shape features from the WP and TZ segmentations, but not for PZ segmentations in all methods.
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