Segmenting Bi-Atrial Structures Using ResNext Based Framework
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912628091125760 |
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| author | Gunawardhana, Malitha Trew, Mark L Sands, Gregory B Zhao, Jichao |
| author_facet | Gunawardhana, Malitha Trew, Mark L Sands, Gregory B Zhao, Jichao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02892 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmenting Bi-Atrial Structures Using ResNext Based Framework Gunawardhana, Malitha Trew, Mark L Sands, Gregory B Zhao, Jichao Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Segmenting Bi-Atrial Structures Using ResNext Based Framework |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.02892 |