mlsanityData to 7 Oct 2026

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

arXiv 2106.09862 ↗First posted 18 Jun 2021cs.CVcited by 26

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Abstract (arXiv)

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 28 of its 202 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

28 of 202 references.

References · 28

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  4. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  5. 2019Deep learning for cardiac image segmentation: A reviewcited by 131
  6. 2019Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challengecited by 100
  7. 2019Deep Learning vs. Traditional Computer Visioncited by 94
  8. 2020Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challengecited by 41
  9. 2021Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentationcited by 28
  10. 2020AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Informationcited by 25
  11. 2019Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Frameworkcited by 19
  12. 2018Multi-Task Learning for Left Atrial Segmentation on GE-MRIcited by 16
16 more
  1. 2020Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attentioncited by 16
  2. 2021AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIscited by 16
  3. 2020Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attentioncited by 15
  4. 2020Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Informationcited by 12
  5. 2018Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Losscited by 8
  6. 2018Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentationcited by 7
  7. 2018Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentationcited by 6
  8. 2018Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRIcited by 6
  9. 2019Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRIcited by 6
  10. 2017Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRIcited by 5
  11. 2018Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentationcited by 3
  12. 2018Atrial fibrosis quantification based on maximum likelihood estimator of multivariate imagescited by 2
  13. 2018Fast quasi-conformal regional flattening of the left atriumcited by 2
  14. 2018Mind the gap: quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path searchcited by 1
  15. 2018V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentationcited by 1
  16. 2018Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-Atlas Selection and Registrationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 5; 2024: 2; 2025: 5; 2026: 8.

26 papers.

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  1. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  2. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  3. 2022MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Imagescited by 29
  4. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
  5. 2023Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentationcited by 7
  6. 2025Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusioncited by 4
  7. 2022UGformer for Robust Left Atrium and Scar Segmentation Across Scannerscited by 3
  8. 2023Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Netcited by 2
  9. 2024Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniquescited by 2
  10. 2025K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Modelcited by 2
  11. 2022Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentationcited by 1
  12. 2022Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Networkcited by 1
14 more
  1. 2023High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI using Online Learning Neural Networkscited by 1
  2. 2024KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentationcited by 1
  3. 2025LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentationcited by 1
  4. 20263D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Maskscited by 1
  5. 2022Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Reviewcited by 0
  6. 2025mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Arrhythmiacited by 0
  7. 2025Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applicationscited by 0
  8. 2026Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learningcited by 0
  9. 2026C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D LGE-MRIcited by 0
  10. 2026Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRIcited by 0
  11. 2026Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Studycited by 0
  12. 2026CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluationscited by 0
  13. 2026Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planningcited by 0
  14. 2026Multicentre Bi-atrial Segmentation from LGE-MRI for Atrial Fibrillation with a 2D and 3D Frameworkcited by 0