Segmenting Bi-Atrial Structures Using ResNext Based Framework

• URL: http://arxiv.org/abs/2503.02892v2
• Date: Wed, 26 Mar 2025 22:43:13 GMT
• Title: Segmenting Bi-Atrial Structures Using ResNext Based Framework
• Authors: Malitha Gunawardhana, Fangqiang Xu, Jichao Zhao,
• Abstract summary: Atrial fibrillation (AF) is the most common cardiac arrhythmia, contributing to mortality, particularly in older populations.<n>Recent research highlights the importance of targeting additional atrial regions, particularly fibrotic areas identified via late gadolinium-enhanced MRI (LGE-MRI)<n>Deep learning techniques, particularly convolutional neural networks (CNNs), have shown promise in automating segmentation.<n>We propose a novel two-stage framework incorporating ResNeXt encoders and a cyclic learning rate to segment both the right atrium (RA) and LA walls and cavities in LGE-MRIs.
• Score: 2.5725730509014353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia, significantly contributing to mortality, particularly in older populations. While pulmonary vein isolation is a standard treatment, its effectiveness is limited in patients with persistent AF. Recent research highlights the importance of targeting additional atrial regions, particularly fibrotic areas identified via late gadolinium-enhanced MRI (LGE-MRI). However, existing manual segmentation methods are time-consuming and prone to variability. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown promise in automating segmentation. However, most studies focus solely on the left atrium (LA) and rely on small datasets, limiting generalizability. In this paper, we propose a novel two-stage framework incorporating ResNeXt encoders and a cyclic learning rate to segment both the right atrium (RA) and LA walls and cavities in LGE-MRIs. Our method aims to improve the segmentation of challenging small structures, such as atrial walls while maintaining high performance in larger regions like the atrial cavities. The results demonstrate that our approach offers superior segmentation accuracy and robustness compared to traditional architectures, particularly for imbalanced class structures.

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