SAMReg：基于ROI对应的SAM图像配准
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
AI总结：
本文提出基于SAM和ROI对应的无训练配准算法SAMReg，在多个医学及非医学数据集上优于传统方法，性能接近弱监督方法。
摘要：
本文描述了一种基于成对感兴趣区域（ROIs）的新型空间对应表示方法，用于医学图像配准。与基于采样位移和空间变换函数的其他对应表示方法相比，本文讨论了所提出的基于ROI的对应方法在图像配准后临床应用潜在益处方面的独特性质。这些益处包括基于学习的图像配准与分割之间的明确联系，这反过来推动了两种使用（预）训练分割网络的图像配准方法。基于分割一切模型（SAM），一种用于分割的视觉基础模型，我们开发了一种新的配准算法SAMReg，该算法不需要任何训练（或训练数据）、基于梯度的微调或提示工程。所提出的SAMReg模型在五个真实世界应用中进行了评估，包括心脏MR和肺部CT的受试者内配准任务，具有挑战性的前列腺MR和视网膜成像的受试者间配准场景，以及使用航空图像配准的非临床示例的额外评估。所提出的方法在包括Dice和靶点配准误差在内的测试指标上优于基于强度的迭代算法和预测DDF的基于学习的网络，并进一步展示了与依赖完全分割训练数据的弱监督配准方法相比具有竞争力的性能。开源代码和示例可在以下网址获取：此https URL。
英文摘要：
This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based on sampled displacements and spatial transformation functions. These benefits include a clear connection between learning-based image registration and segmentation, which in turn motivates two cases of image registration approaches using (pre-)trained segmentation networks. Based on the segment anything model (SAM), a vision foundation model for segmentation, we develop a new registration algorithm SAMReg, which does not require any training (or training data), gradient-based fine-tuning or prompt engineering. The proposed SAMReg models are evaluated across five real-world applications, including intra-subject registration tasks with cardiac MR and lung CT, challenging inter-subject registration scenarios with prostate MR and retinal imaging, and an additional evaluation with a non-clinical example with aerial image registration. The proposed methods outperform both intensity-based iterative algorithms and DDF-predicting learning-based networks across tested metrics including Dice and target registration errors on anatomical structures, and further demonstrates competitive performance compared to weakly-supervised registration approaches that rely on fully-segmented training data. Open source code and examples are available at: https://github.com/sqhuang0103/SAMReg.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
