ArticleIEEE transactions on medical imaging2019
Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 150 citations in OpenAlex.
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- ABR-UNet3D: Aspect-Aware Boundary-Resilient Attention for Robust Cardiac MRI Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.Expert systems with applications · 2025Article
- Automated three-dimensional left atrial analysis on computed tomography angiography: reproducibility and workflow efficiency of eight clinically relevant metrics using a deep learning pipeline.Frontiers in bioengineering and biotechnology · 2025Article
- Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.Computational and structural biotechnology journal · 2025Article
- Artificial intelligence in chronic disease self-management: current applications and future directions.Frontiers in public health · 2025Review
- RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity.Scientific data · 2024Article
- Article
- Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation.Life (Basel, Switzerland) · 2023Review
- Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images.Annals of biomedical engineering · 2023Article
- Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning.Sensors (Basel, Switzerland) · 2023Article
- Automated morphologic analysis of intracranial and extracranial arteries using convolutional neural networks.The British journal of radiology · 2022Article
- The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review.Journal of clinical medicine · 2022Review
- Automatic 3D Surface Reconstruction of the Left Atrium From Clinically Mapped Point Clouds Using Convolutional Neural Networks.Frontiers in physiology · 2022Article
- Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers.European heart journal. Cardiovascular Imaging · 2021Article
- Artificial intelligence with deep learning in nuclear medicine and radiology.EJNMMI physics · 2021Review
- How to do left atrial late gadolinium enhancement: a review.La Radiologia medica · 2021Review
- Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation.The international journal of cardiovascular imaging · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm ×0.625 mm ×1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm
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