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

arXiv: https://arxiv.org/abs/2106.09862 · HTML: https://mlsanity.com/paper/2106.09862/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 202 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 28 of 28)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge](https://mlsanity.com/paper/2006.12434/) (2020)
- [Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation](https://mlsanity.com/paper/2106.08752/) (2021)
- [AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information](https://mlsanity.com/paper/2008.04729/) (2020)
- [Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework](https://mlsanity.com/paper/1902.07877/) (2019)
- [Multi-Task Learning for Left Atrial Segmentation on GE-MRI](https://mlsanity.com/paper/1810.13205/) (2018)
- [Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention](https://mlsanity.com/paper/2002.00440/) (2020)
- [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs](https://mlsanity.com/paper/2106.08727/) (2021)
- [Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention](https://mlsanity.com/paper/2006.13011/) (2020)
- [Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information](https://mlsanity.com/paper/2008.12205/) (2020)
- [Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss](https://mlsanity.com/paper/1812.02518/) (2018)
- [Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation](https://mlsanity.com/paper/1812.05807/) (2018)
- [Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation](https://mlsanity.com/paper/1806.04597/) (2018)
- [Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI](https://mlsanity.com/paper/1808.01673/) (2018)
- [Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI](https://mlsanity.com/paper/1909.01182/) (2019)
- [Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI](https://mlsanity.com/paper/1705.09529/) (2017)
- [Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation](https://mlsanity.com/paper/1812.05802/) (2018)
- [Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images](https://mlsanity.com/paper/1810.09075/) (2018)
- [Fast quasi-conformal regional flattening of the left atrium](https://mlsanity.com/paper/1811.06896/) (2018)
- [Mind the gap: quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search](https://mlsanity.com/paper/1806.06387/) (2018)
- [V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation](https://mlsanity.com/paper/1808.01944/) (2018)
- [Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-Atlas Selection and Registration](https://mlsanity.com/paper/1810.04425/) (2018)

## Built on by (most cited first, 26 of 26)

- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation](https://mlsanity.com/paper/2302.03537/) (2023)
- [Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion](https://mlsanity.com/paper/2501.05241/) (2025)
- [UGformer for Robust Left Atrium and Scar Segmentation Across Scanners](https://mlsanity.com/paper/2210.05151/) (2022)
- [K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model](https://mlsanity.com/paper/2509.25594/) (2025)
- [Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net](https://mlsanity.com/paper/2304.14071/) (2023)
- [Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques](https://mlsanity.com/paper/2407.09561/) (2024)
- [Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation](https://mlsanity.com/paper/2206.05284/) (2022)
- [Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network](https://mlsanity.com/paper/2208.04940/) (2022)
- [High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI using Online Learning Neural Networks](https://mlsanity.com/paper/2312.09387/) (2023)
- [KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation](https://mlsanity.com/paper/2410.21085/) (2024)
- [LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation](https://mlsanity.com/paper/2508.04553/) (2025)
- [3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks](https://mlsanity.com/paper/2601.04588/) (2026)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Arrhythmia](https://mlsanity.com/paper/2508.02274/) (2025)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)
- [Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning](https://mlsanity.com/paper/2603.24985/) (2026)
- [C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D LGE-MRI](https://mlsanity.com/paper/2603.24992/) (2026)
- [Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI](https://mlsanity.com/paper/2603.26186/) (2026)
- [Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2605.06160/) (2026)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (2026)
- [Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning](https://mlsanity.com/paper/2608.21180/) (2026)
- [Multicentre Bi-atrial Segmentation from LGE-MRI for Atrial Fibrillation with a 2D and 3D Framework](https://mlsanity.com/paper/2609.22398/) (2026)

## 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.