融合多种模型提升心房纤维化影像分割精度，助力房颤治疗决策
Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients
- 集成Unet、ResNet等多模型，通过投票机制实现自动双心房分割
- 在200例数据上达88.41%的骰子系数，关键结构误差小于1毫米
- 为房颤消融治疗提供精准解剖依据，适合临床影像分析团队使用
房颤是最常见的心律失常，其临床干预效果受限于对维持心律失常的心房解剖结构理解不足。晚期钆增强MRI（LGE-MRI）已成为评估心房纤维化与瘢痕的关键影像手段，对预测消融疗效至关重要。2024年MICCAI大会的多类别双心房分割（MBAS）挑战赛提供了200例多中心3D LGE-MRI数据集，由专家标注。本文提出一种集成学习方法，融合Unet、ResNet、EfficientNet和VGG等多种模型，实现从LGE-MRI中自动分割左右心房及其壁。在内部测试集上，该模型对左心房壁、右心房腔、左心房腔的骰子相似系数（DSC）分别为88.41%、98.48%、98.45%，95%豪斯多夫距离（HD95）分别为1.07、0.95、0.64毫米。结果表明集成方法显著提升了分割准确性，有助于深化对房颤机制的理解，并支持制定更精准有效的消融策略。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。