# Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning

arXiv: https://arxiv.org/abs/2603.24985 · HTML: https://mlsanity.com/paper/2603.24985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (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)
- [Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention](https://mlsanity.com/paper/2002.00440/) (2020)
- [Meta-learning with implicit gradients in a few-shot setting for medical image segmentation](https://mlsanity.com/paper/2106.03223/) (2021)
- [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024)
- [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)
- [Learning to Segment Medical Images from Few-Shot Sparse Labels](https://mlsanity.com/paper/2108.05476/) (2021)
- [3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks](https://mlsanity.com/paper/2601.04588/) (2026)

## Abstract (arXiv)

Segmenting the left atrial (LA) wall from late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is challenging because of its thin geometry, low contrast, and limited expert annotations. We propose a model-agnostic meta-learning (MAML) framework with a 3D residual U-Net backbone for K-shot (K = 5, 10, 20) LA wall segmentation. The framework is meta-trained on LA wall tasks together with auxiliary LA and right atrial (RA) cavity tasks and uses a boundary-aware composite loss to improve thin-structure delineation. We evaluated MAML on a held-out clean test set and assessed its robustness under an unseen synthetic domain shift and on a local cohort. On the held-out clean test set, MAML outperformed the K-shot fine-tuning baseline at 5-shot, achieving Dice coefficient (DSC) = 0.54 versus 0.48 and Hausdorff distance (HD95) = 4.60 versus 6.40 mm. At 20-shot, MAML approached the fully supervised model trained from scratch, with DSC = 0.59 versus 0.61. Under unseen shifts, performance decreased relative to clean testing but improved consistently as K increased. At 5-shot, MAML achieved DSC = 0.52 and HD95 = 5.02 mm under the unseen synthetic shift, and DSC = 0.50 and HD95 = 5.43 mm on the local cohort. These results suggest that meta-learning can improve thin-wall delineation in low-shot adaptation and may reduce the annotation burden for atrial remodeling assessment.