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Abstract #3498

Automated Zonal Prostate Segmentation with 2.5D Convolutional Neural Networks

Alex Bratt1, Kevin Seals2, and Daniel Margolis3

1Department of Radiology, Weill Cornell Medicine/New York Presbyterian Hospital, New York, NY, United States, 2Department of Radiology, University of California, Los Angeles, Los Angeles, CA, United States, 3Department of Radiology, Weill Cornell Medicine, New York, NY, United States

Accurate delineation of anatomical boundaries on prostate MR is crucial for cancer staging and standardized assessment. Unfortunately, manual prostate segmentation is time consuming and prone to inter-rater variability while existing automated segmentation software is expensive and inaccurate. We demonstrate a novel fully-automated zonal prostate segmentation method that is fast and accurate using a convolutional neural network. The network is trained using a dataset of 149 T2-weighted prostate MR volumes that were manually annotated by radiologists. Our method improves upon prior related work, achieving a full-gland Dice score of 0.92 and zonal Dice score of 0.88.

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