Huang, S; Xu, T; Yan, W; Barratt, D; Hu, Y; (2026) Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model. In: Gee, James C and Alexander, Daniel C and Hong, Jaesung and Iglesias, Juan Eugenio and Sudre, Carole H and Venkataraman, Archana and Golland, Polina and Kim, Jong Hyo and Park, Jinah, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2025. 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part IV. (pp. pp. 467-477). Springer: Cham, Switzerland.

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Abstract

Establishing pixel/voxel-level or region-level correspondences is the core challenge in image registration. The latter, also known as region-based correspondence representation, leverages paired regions of interest (ROIs) to enable regional matching while preserving fine-grained capability at pixel/voxel level. Traditionally, this representation is implemented via two steps: segmenting ROIs in each image then matching them between the two images. In this paper, we simplify this into one step by directly “searching for corresponding prompts”, using extensively pre-trained segmentation models (e.g., SAM) for a training-free registration approach, PromptReg. Firstly, we introduce the “corresponding prompt problem”, which aims to identify a corresponding Prompt Y in Image Y for any given visual Prompt X in Image X, such that the two respectively prompt-conditioned segmentations are a pair of corresponding ROIs from the two images. Secondly, we present an “inverse prompt” solution that generates primary and optionally auxiliary prompts, inverting Prompt X into the prompt space of Image Y. Thirdly, we propose a novel registration algorithm that identifies multiple paired corresponding ROIs by marginalizing the inverted Prompt X across both prompt and spatial dimensions. Comprehensive experiments are conducted on five applications of registering 3D prostate MR, 3D abdomen MR, 3D lung CT, 2D histopathology and, as a non-medical example, 2D aerial images. Based on metrics including Dice and target registration errors on anatomical structures, the proposed registration outperforms both intensity-based iterative algorithms and learning-based DDF-predicting networks, even yielding competitive performance with weakly-supervised approaches that require fully-segmented training data.

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