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.

翻译：心房颤动（AF）是最常见的心律失常类型，与发病率和死亡率的增加相关。当前针对AF的临床干预措施效果往往受限于对维持该心律失常的心房解剖结构认识不足。钆延迟增强磁共振成像（LGE-MRI）已成为评估心房纤维化和瘢痕的关键成像模态，这些是预测AF患者消融手术成功率的重要标志。MICCAI 2024多类别双心房分割（MBAS）挑战赛旨在利用由专家标注的200例多中心3D LGE-MRI综合数据集，提升左右心房及其心房壁的分割精度。本研究提出了一种集成方法，整合了包括Unet、ResNet、EfficientNet和VGG在内的多种机器学习模型，以从LGE-MRI数据中实现自动双心房分割。该集成模型在左心房壁、右心房壁、右心房腔和左心房腔上使用戴斯相似系数（DSC）和95%豪斯多夫距离（HD95）进行评估。在内部测试数据集上，模型分别取得了88.41%、98.48%、98.45%的DSC和1.07、0.95、0.64的HD95。这证明了集成模型在提升分割准确性方面的有效性。该方法有助于推进对AF的理解，并支持开发更具针对性和更有效的消融策略。