Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

TL;DR

This paper introduces a deep learning approach that predicts atrial fibrillation recurrence directly from MRI images without pre-processing, matching traditional methods while reducing human effort and computational complexity.

Contribution

It presents a novel deep network model with data augmentation for estimating shape descriptors from MRI images, bypassing segmentation and correspondence steps.

Findings

Deep learning method achieves similar accuracy to state-of-the-art.

Eliminates need for image segmentation and correspondence optimization.

Reduces human labor and computational resources.

Abstract

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.