Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients

TL;DR

This paper introduces an ensemble machine learning approach combining multiple models to improve the accuracy of bi-atrial segmentation in LGE-MRI images for atrial fibrillation patients, aiding better diagnosis and treatment planning.

Contribution

It presents a novel ensemble method integrating Unet, ResNet, EfficientNet, and VGG models for automatic bi-atrial segmentation in LGE-MRI, demonstrating improved accuracy over existing techniques.

Findings

Achieved high DSC scores for atrial wall segmentation.

Demonstrated low Hausdorff distance indicating precise boundary delineation.

Validated effectiveness on multi-center dataset.

Abstract

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia and is associated with increased morbidity and mortality. The effectiveness of current clinical interventions for AF is often limited by an incomplete understanding of the atrial anatomical structures that sustain this arrhythmia. Late Gadolinium-Enhanced MRI (LGE-MRI) has emerged as a critical imaging modality for assessing atrial fibrosis and scarring, which are essential markers for predicting the success of ablation procedures in AF patients. The Multi-class Bi-Atrial Segmentation (MBAS) challenge at MICCAI 2024 aims to enhance the segmentation of both left and right atria and their walls using a comprehensive dataset of 200 multi-center 3D LGE-MRIs, labelled by experts. This work presents an ensemble approach that integrates multiple machine learning models, including Unet, ResNet, EfficientNet and VGG, to perform automatic bi-atrial segmentation from LGE-MRI data. The ensemble model was evaluated using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD95) on the left & right atrium wall, right atrium cavity, and left atrium cavity. On the internal testing dataset, the model achieved a DSC of 88.41%, 98.48%, 98.45% and an HD95 of 1.07, 0.95, 0.64 respectively. This demonstrates the effectiveness of the ensemble model in improving segmentation accuracy. The approach contributes to advancing the understanding of AF and supports the development of more targeted and effective ablation strategies.