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Abstract #1714

A Shape Attentive Convolutional Neural Network for Improving the Generalizability of CMR Image Segmentation

Xuan Wang1,2, Steven G Lloyd3, Himanshu Gupta4, Louis J Dell’Italia3, and Thomas S Denney 1,2
1Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 2Neuroimaging Center, Auburn University, Auburn, AL, United States, 3Division of Cardiovascular Disease, University of Alabama, Birmingham, AL, United States, 4Heart and Vascular Institute, Valley Health System Paramus, Ridgewood, NJ, United States

Synopsis

Keywords: Analysis/Processing, Segmentation, Cardiac MRI

Motivation: Collecting contoured medical data is costly, and the performance of deep learning networks can depend on the data acquisition site.

Goal(s): Design a novel generalizable network to automatically segment the left and right ventricles and myocardium from cardiac magnetic resonance (CMR) data.

Approach: : Combine two CNN-based attention mechanisms and a Transformer attention mechanism to improve performance, test the network on a publicly available CMR dataset measuring segmentation accuracy for each structure and at base, mid, and apex levels.

Results: Our network performs better than previous methods averaged over each structure and considerably better at the base and apex levels.

Impact: Our network can be trained and validated on CMR data from one site and can accurately segment CMR data from other sites.

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Keywords