Abstract
Image registration is a fundamental task in medical image analysis. Deformations are often closely related to the morphological characteristics of tissues, making accurate feature extraction crucial. Recent weakly supervised methods improve registration by incorporating anatomical priors such as segmentation masks or landmarks, either as inputs or in the loss function. However, such weak labels are often not readily available, limiting their practical use. Motivated by the strong representation learning ability of visual foundation models, this paper introduces SAMIR, an efficient medical image registration framework that utilizes the Segment Anything Model (SAM) to enhance feature extraction. SAM is pretrained on large-scale natural image datasets and can learn robust, general-purpose visual representations. Rather than using raw input images, we design a task-specific adaptation pipeline using SAM's image encoder to extract structure-aware feature embeddings, enabling more accurate modeling of anatomical consistency and deformation patterns. We further design a lightweight 3D head to refine features within the embedding space, adapting to local deformations in medical images. Additionally, we introduce a Hierarchical Feature Consistency Loss to guide coarse-to-fine feature matching and improve anatomical alignment. Extensive experiments demonstrate that SAMIR significantly outperforms state-of-the-art methods on benchmark datasets for both intra-subject cardiac image registration and inter-subject abdomen CT image registration, achieving performance improvements of 2.68% on ACDC and 6.44% on the abdomen dataset. The source code will be publicly available on GitHub following the acceptance of this paper.
Abstract (translated)
医学图像分析中，图像配准是一项基本任务。变形通常与组织的形态特征密切相关，因此准确地提取特征至关重要。最近的一些弱监督方法通过在输入或损失函数中加入解剖先验（如分割掩码或地标）来改进注册效果。然而，这种弱标签往往不易获得，限制了它们的实际应用。鉴于视觉基础模型强大的表示学习能力，本文介绍了一种名为SAMIR的高效医学图像配准框架，该框架利用Segment Anything Model (SAM) 来增强特征提取。SAM预先在大规模自然图像数据集上训练，并能学习到稳健且通用的视觉表征。 与使用原始输入图像不同，我们设计了一个特定任务适应性流水线，通过使用SAM的图像编码器来提取结构感知特征嵌入，从而能够更准确地建模解剖一致性和变形模式。此外，我们还设计了一种轻量级3D头部，在嵌入空间内细化特性，并适应医学图像中的局部变形。同时，引入了分层特征一致性损失以引导从粗到细的特征匹配并提高解剖对齐。 广泛的实验表明，SAMIR在用于心脏图像配准和腹部CT图像配准的基准数据集上显著优于现有最佳方法，分别在ACDC数据集中提高了2.68%，在腹部数据集中提高了6.44%。论文被接受后，源代码将通过GitHub公开发布。
URL
https://arxiv.org/abs/2509.13629
