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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here