Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
This work addresses the challenge of reducing human labor and expertise required for population-level shape analysis in cardiac MRI for atrial fibrillation patients, though it is incremental as it matches rather than surpasses existing performance.
The paper tackles the problem of estimating atrial fibrillation recurrence after ablation by predicting shape descriptors directly from MRI images, eliminating the need for manual image segmentation and correspondence optimization. Results show that the proposed deep learning method produces statistically similar outcomes to the state-of-the-art approach.
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.