  Left atrium shape has been shown to be an independent predictor of recurrence
after atrial fibrillation (AF) ablation. Shape-based representation is
imperative to such an estimation process, where correspondence-based
representation offers the most flexibility and ease-of-computation for
population-level shape statistics. Nonetheless, population-level shape
representations in the form of image segmentation and correspondence models
derived from cardiac MRI require significant human resources with sufficient
anatomy-specific expertise. In this paper, we propose a machine learning
approach that uses deep networks to estimate AF recurrence by predicting shape
descriptors directly from MRI images, with NO image pre-processing involved. We
also propose a novel data augmentation scheme to effectively train a deep
network in a limited training data setting. We compare this new method of
estimating shape descriptors from images with the state-of-the-art
correspondence-based shape modeling that requires image segmentation and
correspondence optimization. Results show that the proposed method and the
current state-of-the-art produce statistically similar outcomes on AF
recurrence, eliminating the need for expensive pre-processing pipelines and
associated human labor.
