Inter- and intra-observer variability are current limitations of radiological reading of multiparametric MR images of the prostate. Deep learning (DL)-based segmentation has proven to provide good performance, but little is known about the repeatability of these methods. In this work, we investigated the intra-patient repeatability of shape features for DL segmentation methods of the prostate on T2-weighted MR images and compared it to manual segmentations. We found that the repeatability of the investigated methods is excellent for most of the investigated shape features.
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