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Abstract #0635

DeepVentricle: A Fully Convolutional Neural Network for Automating Functional Measurements in Cardiac MR

Hok Kan Lau1, Jesse Lieman-Sifry1, Matthieu Le1, Sean Sall1, John Axerio-Cilies1, Dominik Fleischmann2, Aya Kino3, Frandics Chan2, and Daniel Golden1

1Arterys, Inc, San Francisco, CA, United States, 2General Radiology, Stanford University School of Medicine, Stanford, CA, 3Radiology, Stanford University School of Medicine, Stanford, CA

We present DeepVentricle, an automated approach to ventricular segmentation in cardiac MR. DeepVentricle uses a fully convolutional neural network to simultaneously perform semantic segmentation of the left ventricle (LV) and right ventricle (RV) endocardium, and LV epicardium; segmentations are then used to estimate ejection fraction and myocardial mass. We show that the error rates of LV ejection fraction and mass are within the expected range of expert annotator inter-rater variation. This suggests that contours calculated using DeepVentricle could be useful on their own or as an initial estimate for clinicians as part of their semi-automated annotation workflow.

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