用自监督模型DINOv2实现少量标注下心脏左心房精准分割
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images
- 基于DINOv2视觉变压器，通过自监督学习实现MRI图像端到端分割
- 在有限标注数据下达到0.871的平均Dice分数和0.792的Jaccard指数
- 少样本学习表现优于基线模型，适合临床影像数据稀缺场景
术前MRI中左心房（LA）的精准分割对房颤诊断、治疗规划及手术支持至关重要。尽管深度学习模型在医学图像分割中起关键作用，但通常依赖大量人工标注数据。通过更大规模数据训练的基础模型可减少这种依赖，提升迁移能力与鲁棒性。本文探索了在自然图像上训练的自监督视觉变压器DINOv2在MRI图像中用于左心房分割的可行性。由于左心房解剖结构复杂、边界纤薄且标注数据有限，分割难度大。实验表明，经过端到端微调，DINOv2实现了0.871的平均Dice分数与0.792的Jaccard指数；在不同数据量与患者数的少样本学习设置中，其性能始终优于基线模型。结果表明，DINOv2能在标注数据有限的情况下有效适配MRI图像，具备作为医疗影像分割有力工具的潜力，推动其在医学领域的更广泛应用。
原文摘要 · Abstract (English)
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.
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