# AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs

arXiv: https://arxiv.org/abs/2106.08727 · HTML: https://mlsanity.com/paper/2106.08727/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Improving the generalizability of convolutional neural network-based segmentation on CMR images](https://mlsanity.com/paper/1907.01268/) (2019)
- [Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework](https://mlsanity.com/paper/1902.07877/) (2019)
- [Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention](https://mlsanity.com/paper/2006.13011/) (2020)
- [Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging](https://mlsanity.com/paper/2011.00739/) (2020)
- [Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information](https://mlsanity.com/paper/2008.12205/) (2020)
- [Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation](https://mlsanity.com/paper/2012.13871/) (2020)

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

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [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)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [vMFNet: Compositionality Meets Domain-generalised Segmentation](https://mlsanity.com/paper/2206.14538/) (2022)
- [Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data](https://mlsanity.com/paper/2109.08311/) (2021)
- [Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration](https://mlsanity.com/paper/2111.07716/) (2021)
- [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)
- [Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network](https://mlsanity.com/paper/2208.04940/) (2022)
- [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)
- [A Survey on Causal Representation Learning and Future Work for Medical Image Analysis](https://mlsanity.com/paper/2210.16034/) (2022)
- [CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations](https://mlsanity.com/paper/2606.00123/) (2026)

## Abstract (arXiv)

Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However, automatic LA segmentation from LGE MRI is still challenging, due to the poor image quality, high variability in LA shapes, and unclear LA boundary. Though deep learning-based methods can provide promising LA segmentation results, they often generalize poorly to unseen domains, such as data from different scanners and/or sites. In this work, we collect 210 LGE MRIs from different centers with different levels of image quality. To evaluate the domain generalization ability of models on the LA segmentation task, we employ four commonly used semantic segmentation networks for the LA segmentation from multi-center LGE MRIs. Besides, we investigate three domain generalization strategies, i.e., histogram matching, mutual information based disentangled representation, and random style transfer, where a simple histogram matching is proved to be most effective.