# Segmenting Bi-Atrial Structures Using ResNext Based Framework

arXiv: https://arxiv.org/abs/2503.02892 · HTML: https://mlsanity.com/paper/2503.02892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [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)

## Abstract (arXiv)

Atrial Fibrillation (AF), the most common sustained cardiac arrhythmia worldwide, increasingly requires accurate bi-atrial structural assessment to guide ablation strategies, particularly in persistent AF. Late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) enables visualisation of atrial fibrosis, but precise manual segmentation remains time-consuming, operator-dependent, and prone to variability. We propose TASSNet, a novel two-stage deep learning framework for fully automated segmentation of both left atrium (LA) and right atrium (RA), including atrial walls and cavities, from 3D LGE-MRI. TASSNet introduces two main innovations: (i) a ResNeXt-based encoder to enhance feature extraction from limited medical datasets, and (ii) a cyclical learning rate schedule to address convergence instability in highly imbalanced, small-batch 3D segmentation tasks. We evaluated our method on two datasets, one of which was completely out-of-distribution, without any additional training. In both cases, TASSNet successfully segmented atrial structures with high accuracy. These results highlight TASSNet's potential for robust and reproducible bi-atrial segmentation, enabling advanced fibrosis quantification and personalised ablation planning in clinical AF management.