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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