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Abstract #4735

Prostate and peripheral zone segmentation on multi-vendor MRIs using Deep Learning

Olmo Zavala-Romero1, Adrian L. Breto1, Nicole Gautney1, Yu-Cherng C. Chang1, Alan Dal Pra1, Mattew C Abramowitz1, Alan Pollack1, and Radka Stoyanova1

1Radiation Oncology, University of Miami, Miami, FL, United States

A Deep Learning algorithm for automatic segmentation of the prostate and its peripheral zone (PZ) is investigated across MR images from two MRI vendors. The proposed architecture is a 3D U-net that uses axial, coronal, and sagittal MRI series as input. When trained with Siemens MRI, the network achieves a Dice similarity coefficient (DSC) of .91 and .76 for the segmentation of the prostate and the PZ respectively. However, the network performs poorly on a GE dataset. Combining images from different MRI vendors is of paramount importance to pursue a universal algorithm for prostate and PZ segmentation.

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