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Abstract #4105

The repeatability of deep learning-based segmentation of the prostate, peripheral and transition zones on T2-weighted MR images

Mohammed R. S. Sunoqrot1, Kirsten M. Selnæs1,2, Elise Sandsmark2, Sverre Langørgen2, Helena Bertilsson3,4, Tone F. Bathen1,2, and Mattijs Elschot1,2
1Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technolog, Trondheim, Norway, 2Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, 3Department of Cancer Research and Molecular, NTNU, Norwegian University of Science and Technolog, Trondheim, Norway, 4Department of Urology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

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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