Atrial scars segmentation via potential learning in the graph-cuts framework

TL;DR

This paper introduces a fully automated atrial scar segmentation method using deep learning-enhanced graph-cuts on LGE MRI data, improving accuracy and enabling better AF diagnosis.

Contribution

The study presents a novel automated segmentation approach combining deep neural networks with graph-cuts, leveraging surface mesh features for improved atrial scar analysis.

Findings

Performance improved with more training patches

Segmentation accuracy achieved a Dice score of 0.570

Method outperforms existing manual delineation techniques

Abstract

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging. In this study, we pro-posed a fully automated method based on the graph-cuts framework, where the potential of the graph is learned on a surface mesh of the left atrium (LA) using an equidistant projection and a Deep Neural Network (DNN). For validation, we employed 100 datasets with manual delineation. The results showed that the performance of the proposed method improved and converged with respect to the increased size of training patches, which provide important features of the structural and texture information learned by the DNN. The segmentation could be further improved when the contribution from the t-link and n-link is balanced, thanks to inter-relationship learned by the DNN for the graph-cuts algorithm. Compared with the published methods which mostly acquired manual delineation of the LA or LA wall, our method is fully automatic and demonstrated evidently better results with statistical significance. Finally, the accuracy of quantifying the scars assessed by the Dice score was 0.570. The results are promising and the method can be useful in diagnosis and prognosis of AF.