# Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

arXiv: https://arxiv.org/abs/2002.00440 · HTML: https://mlsanity.com/paper/2002.00440/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021)
- [Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives](https://mlsanity.com/paper/2106.15707/) (2021)
- [Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data](https://mlsanity.com/paper/2109.08311/) (2021)
- [JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets](https://mlsanity.com/paper/2105.00234/) (2021)
- [Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI](https://mlsanity.com/paper/2211.06247/) (2022)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification](https://mlsanity.com/paper/2203.10690/) (2022)
- [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)
- [Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network](https://mlsanity.com/paper/2208.04940/) (2022)
- [LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation](https://mlsanity.com/paper/2508.04553/) (2025)
- [Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping](https://mlsanity.com/paper/2104.13214/) (2021)
- [Synthesis of Late Gadolinium Enhancement Images via Implicit Neural Representations for Cardiac Scar Segmentation](https://mlsanity.com/paper/2602.11942/) (2026)
- [Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning](https://mlsanity.com/paper/2603.24985/) (2026)
- [Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI](https://mlsanity.com/paper/2603.26186/) (2026)

## Abstract (arXiv)

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.