# Multi-Task Learning for Left Atrial Segmentation on GE-MRI

arXiv: https://arxiv.org/abs/1810.13205 · HTML: https://mlsanity.com/paper/1810.13205/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

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

- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021)
- [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets](https://mlsanity.com/paper/2105.00234/) (2021)
- [Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.11604/) (2023)
- [Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications](https://mlsanity.com/paper/2309.03837/) (2023)
- [Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs](https://mlsanity.com/paper/2407.14994/) (2024)
- [UGformer for Robust Left Atrium and Scar Segmentation Across Scanners](https://mlsanity.com/paper/2210.05151/) (2022)
- [Dynamic Position Transformation and Boundary Refinement Network for Left Atrial Segmentation](https://mlsanity.com/paper/2407.05505/) (2024)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation](https://mlsanity.com/paper/2601.15408/) (2026)
- [Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images](https://mlsanity.com/paper/2203.00641/) (2022)
- [Complementary consistency semi-supervised learning for 3D left atrial image segmentation](https://mlsanity.com/paper/2210.01438/) (2022)
- [TMS-Net: A Segmentation Network Coupled With A Run-time Quality Control Method For Robust Cardiac Image Segmentation](https://mlsanity.com/paper/2212.10877/) (2022)
- [SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images](https://mlsanity.com/paper/2511.08626/) (2025)

## Abstract (arXiv)

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/.