Key result
Deep learning pipeline biAtriaNet achieves accurate bi-atrial segmentation with a ~91% LA Dice score.
Why the study?
Using LGE-MRI to guide AF ablation has shown variable results due to the lack of robust segmentation methods for atrial anatomy and fibrosis, and the exclusion of the right atrium.
Does biAtriaNet accurately automate bi-atrial segmentation and biomarker extraction from LGE-MRIs compared to expert annotations?
Does biAtriaNet accurately automate bi-atrial segmentation and biomarker extraction from LGE-MRIs compared to expert annotations?
The biAtriaNet deep learning pipeline provides accurate, automated bi-atrial segmentation and biomarker extraction from LGE-MRIs, potentially aiding targeted atrial fibrillation ablation strategies.
Answers are grounded in this paper and cite their sources.
Supports automated bi-atrial analysis in AF imaging; hypothesis-generating pending prospective clinical validation.
Atrial fibrillation (AF) is associated with progressive structural remodeling of the atria, including chamber dilation, fibrosis, and variations in atrial wall thickness (AWT). Late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) has been used to quantify left atrium (LA) fibrosis for guiding adjunctive ablation beyond pulmonary vein isolation, though results have varied. A major limitation is the lack of a robust segmentation method for accurately assessing both atrial anatomy and fibrosis, coupled with the exclusion of the right atrium (RA) in the analysis. This study introduces biAtriaNet, a deep learning pipeline developed to automate segmentation of both LA and RA and to evaluate atrial fibrosis, AWT, and chamber diameter and volume from LGE-MRIs to support targeted AF ablation. biAtriaNet was trained and validated on 2D cine-MRIs from 4860 UK Biobank participants and 3D LGE-MRIs from 60 AF patients from the University of Utah, with independent testing on 11 3D LGE-MRIs at Waikato Hospital, New Zealand. The biAtriaNet consists of two CNNs based on a modified U-Net architecture with residual connections and batch normalization, optimized based on prior global benchmark study. This approach achieved accurate, consistent segmentation and biomarker extraction in UK Biobank and Utah datasets, validated against expert annotations. Additionally, biAtriaNet showed high transferability to independent datasets, achieving Dice scores of 91.1% for LA and 88.6% for RA. Chamber volume estimates closely matched ground truth values (LA: 89.8 ± 33.0 ml versus 91.1 ± 41.2 ml; RA: 70.8 ± 16.9 ml versus 72.3 ± 20.5 ml) with >90% accuracy in chamber measurements. AWT accuracies were 95.9% for LA and 94.6% for RA, while fibrosis estimates showed Kolmogorov-Smirnov correlations of 86.3% (LA) and 90.6% (RA) (p < 0.05). By enabling robust bi-atrial segmentation and biomarker extraction from LGE-MRIs, biAtriaNet has the potential to enhance patient-specific AF treatment strategies.
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Feng et al. (2025) studied Atrial fibrillation (n=4,931). biAtriaNet (deep learning pipeline) vs. Expert annotations (ground truth) was evaluated on Segmentation accuracy (Dice scores) and biomarker extraction accuracy (p=< 0.05). The biAtriaNet deep learning pipeline achieved accurate bi-atrial segmentation on independent datasets, with Dice scores of 91.1% for the left atrium and 88.6% for the right atrium.
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