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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.24985 |
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| _version_ | 1866916037625118720 |
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| author | Al-Sanaani, Yusri Thornhill, Rebecca Nery, Pablo Pena, Elena deKemp, Robert Redpath, Calum Birnie, David Rajan, Sreeraman |
| author_facet | Al-Sanaani, Yusri Thornhill, Rebecca Nery, Pablo Pena, Elena deKemp, Robert Redpath, Calum Birnie, David Rajan, Sreeraman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24985 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning Al-Sanaani, Yusri Thornhill, Rebecca Nery, Pablo Pena, Elena deKemp, Robert Redpath, Calum Birnie, David Rajan, Sreeraman Computer Vision and Pattern Recognition 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. |
| title | Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.24985 |