用医学影像专精的自监督预训练，提升3D医疗图像配准精度与效率
Effective Feature Learning for 3D Medical Registration via Domain-Specialized DINO Pretraining
- 在3D医学影像上直接做DINO式自监督预训练，学习适合配准的密集特征
- 在跨患者腹部配准任务中优于DINOv2和现有模型，且推理耗时更低
- 无需标注数据，适合临床部署，尤其适用于多模态、跨设备场景
医学图像配准是临床影像工作流的关键环节，支持纵向评估、多模态数据融合及术中导航。基于强度的方法常受扫描仪差异和复杂解剖形变影响，而基于特征的方法通过语义感知表示提升了鲁棒性。本文研究在3D医学影像上直接进行DINO风格的自监督预训练，旨在学习适用于可变形配准的密集体素特征。我们在跨患者腹部配准任务上评估了该方法在MRI与CT模态下的表现。结果表明，领域专精的预训练模型优于在大规模自然图像上训练的DINOv2模型，且推理资源消耗显著降低。此外，在域外评估中仍表现更优，验证了任务无关但聚焦医学影像的预训练对实现鲁棒高效3D图像配准的价值。
原文摘要 · Abstract (English)
Medical image registration is a critical component of clinical imaging workflows, enabling accurate longitudinal assessment, multi-modal data fusion, and image-guided interventions. Intensity-based approaches often struggle with interscanner variability and complex anatomical deformations, whereas feature-based methods offer improved robustness by leveraging semantically informed representations. In this work, we investigate DINO-style self-supervised pretraining directly on 3D medical imaging data, aiming to learn dense volumetric features well suited for deformable registration. We assess the resulting representations on challenging interpatient abdominal registration task across both MRI and CT modalities. Our domain-specialized pretraining outperforms the DINOv2 model trained on a large-scale collection of natural images, while requiring substantially lower computational resources at inference time. Moreover, it surpasses established registration models under out-of-domain evaluation, demonstrating the value of task-agnostic yet medical imaging-focused pretraining for robust and efficient 3D image registration.
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