Accurate left atrium (LA) segmentation from pre-operative scans is crucial for diagnosing atrial fibrillation, treatment planning, and supporting surgical interventions. While deep learning models are key in medical image segmentation, they often require extensive manually annotated data. Foundation models trained on larger datasets have reduced this dependency, enhancing generalizability and robustness through transfer learning. We explore DINOv2, a self-supervised learning vision transformer trained on natural images, for LA segmentation using MRI. The challenges for LA's complex anatomy, thin boundaries, and limited annotated data make accurate segmentation difficult before & during the image-guided intervention. We demonstrate DINOv2's ability to provide accurate & consistent segmentation, achieving a mean Dice score of .871 & a Jaccard Index of .792 for end-to-end fine-tuning. Through few-shot learning across various data sizes & patient counts, DINOv2 consistently outperforms baseline models. These results suggest that DINOv2 effectively adapts to MRI with limited data, highlighting its potential as a competitive tool for segmentation & encouraging broader use in medical imaging.

翻译：从术前扫描中准确分割左心房（LA）对于诊断心房颤动、制定治疗计划以及支持外科干预至关重要。虽然深度学习模型在医学图像分割中发挥着关键作用，但它们通常需要大量手动标注数据。基于更大数据集训练的基础模型通过迁移学习减少了对标注数据的依赖，增强了模型的泛化能力和鲁棒性。本研究探讨了DINOv2（一种在自然图像上训练的自监督学习视觉Transformer模型）在MRI图像中进行LA分割的应用。LA解剖结构复杂、边界薄且标注数据有限，这些挑战使得在图像引导干预之前和期间实现准确分割变得困难。我们证明了DINOv2能够提供准确且一致的分割结果：在端到端微调中，其平均Dice系数达到0.871，Jaccard指数达到0.792。通过在不同数据量和患者数量下进行少样本学习，DINOv2始终优于基线模型。这些结果表明，DINOv2能够有效适应有限数据的MRI分割任务，突显了其作为医学图像分割竞争性工具的潜力，并鼓励在医学影像领域更广泛地应用该模型。