Abstract
Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deformable registration methods rely solely on intensity-based similarity metrics, lacking explicit anatomical knowledge, which limits their accuracy and robustness. Vision foundation models, such as the Segment Anything Model (SAM), can generate high-quality segmentation masks that provide explicit anatomical structure knowledge, addressing the limitations of traditional methods that depend only on intensity similarity. Based on this, we propose a novel SAM-assisted registration framework incorporating prototype learning and contour awareness. The framework includes: (1) Explicit anatomical information injection, where SAM-generated segmentation masks are used as auxiliary inputs throughout training and testing to ensure the consistency of anatomical information; (2) Prototype learning, which leverages segmentation masks to extract prototype features and aligns prototypes to optimize semantic correspondences between images; and (3) Contour-aware loss, a contour-aware loss is designed that leverages the edges of segmentation masks to improve the model's performance in fine-grained deformation fields. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods across multiple datasets, particularly in challenging scenarios with complex anatomical structures and ambiguous boundaries. Our code is available at this https URL.
Abstract (translated)
医学图像配准是医学图像分析中的基本任务，旨在建立成对图像之间的空间对应关系。然而，现有的无监督可变形配准方法仅依赖于基于强度的相似性度量，缺乏显式的解剖学知识，这限制了它们的准确性和鲁棒性。视觉基础模型，如Segment Anything Model (SAM)，可以生成高质量的分割掩码，提供明确的解剖结构知识，从而克服了传统方法单纯依靠强度相似性的局限性。基于此，我们提出了一种新的SAM辅助配准框架，该框架结合了原型学习和轮廓感知功能。该框架包括： 1. 显式解剖信息注入：使用由SAM生成的分割掩码作为在整个训练和测试过程中使用的辅助输入，以确保解剖学信息的一致性。 2. 原型学习：利用分割掩码提取原型特征，并对齐这些原型以优化图像之间的语义对应关系。 3. 轮廓感知损失：设计了一种轮廓感知损失函数，该函数利用分割掩码的边缘来提高模型在精细变形场中的性能。 广泛的实验表明，所提出的框架在多个数据集上显著优于现有方法，特别是在具有复杂解剖结构和模糊边界等挑战性场景中。我们的代码可在[此链接](https://this https URL)获取。
URL
https://arxiv.org/abs/2502.11440
