Abstract #4513

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

Jakob Meglič^1,2, Mohammed R. S. Sunoqrot^1,3, Tone F. Bathen^1,3, and Mattijs Elschot^1,3

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, ²Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia, ³Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Synopsis

Keywords: Machine Learning/Artificial Intelligence, ProstateProstate 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.

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