Key result
Evaluation of 13 segmentation methods on 175 MRI scans showed that U-Net and state-space models achieved higher accuracy, though atrial wall delineation was highly sensitive to image degradation.
Why the study?
Existing benchmarks focused only on the left atrial cavity, overlooking bi-atrial anatomy and thin atrial walls critical for substrate-guided ablation planning in atrial fibrillation.
The MBAS2024 challenge establishes a foundational benchmark for bi-atrial segmentation from LGE-MRI, highlighting that model architecture and hybrid labeling strategies are key drivers of segmentation accuracy.
U-Net and state-space models merit priority in atrial MRI segmentation research; leaves open robustness under clinical image degradation.
Atrial fibrillation (AF), the most common cardiac arrhythmia, affects one in three adults over 45 years of age. Improving its treatment requires a better understanding of bi-atrial anatomy. Existing benchmarks have focused on the left atrial (LA) cavity, overlooking the fundamental challenges posed by bi-atrial anatomy, most notably the thin atrial walls, which are critical for substrate-guided ablation planning in patients with atrial fibrillation. To address these limitations, the Multi-class Bi-Atrial Segmentation 2024 Challenge (MBAS2024) introduced the first large-scale, multi-class benchmark for simultaneous segmentation of the LA cavity, right atrial (RA) cavity, and bi-atrial walls from late gadolinium-enhanced (LGE) MRI. We systematically evaluated 13 state-of-the-art methods on the world's largest curated bi-atrial dataset, comprising 175 3D multi-center scans with expert-validated annotations, providing a comprehensive assessment of current methodological capabilities and limitations. Key findings include: segmentation of the LA and RA cavities is generally robust to image quality, whereas atrial wall delineation is highly sensitive to image degradation. Performance varies across centers, indicating limited generalization of atrial wall segmentation across different acquisition protocols. Model architecture, rather than hyperparameter tuning, is the primary driver of performance, with U-Net-based models and emerging state-space models (e.g., UMambaBot) achieving higher accuracy at modest computational cost. Segmentation accuracy also varies along the slice dimension, with central slices segmented more reliably. Finally, hybrid labeling strategies-separating LA and RA cavities while merging bi-atrial walls into a single class-consistently improve performance. The MBAS2024 challenge establishes a foundational benchmark for bi-atrial segmentation, providing validated baselines and actionable insights to guide the development of clinically relevant, efficient, and anatomically aware segmentation algorithms to improve targeted ablation in patients with AF.
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Xu et al. (2026) studied Atrial fibrillation (n=175). State-of-the-art segmentation methods (e.g., U-Net, UMambaBot) was evaluated on Simultaneous segmentation of the LA cavity, RA cavity, and bi-atrial walls. Evaluation of 13 segmentation methods on 175 MRI scans showed that U-Net and state-space models achieved higher accuracy, though atrial wall delineation was highly sensitive to image degradation.
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