PromptReg: Interactive Registration by “Corresponding Prompts” for Segment Anything Model (SAM)

Shiqi Huang, Tingfa Xu, Jianan Li, Shaheer U. Saeed, Ziyi Shen, Dean C. Barratt, Yipeng Hu

Abstract: Effectively establishing correspondence between two images is at the centre of image registration methods. Spatially omnipresent representations, including dense displacement fields (DDFs) and spatial (non-)rigid transformations, have been used to parameterise such correspondence. Alternatively, region-based representation uses paired regions of interest (ROIs) to represent region-level correspondence, while retaining its local and dense representation capability at pixel/voxel level if required. Thus, registration can be re-envisioned as a problem of segmenting corresponding paired ROIs in the to-be-registered images. In this work, we utilize models such as SAM, which are pre-trained on substantive datasets, to segment ROIs of the same class from two images, for a new training-free, non-iterative registration algorithm. First, a “corresponding prompt problem” is posed to find a corresponding Prompt Y on Image Y, given any vision Prompt X on Image X, such that the two respectively prompt-conditioned segmentations are a pair of corresponding ROIs from the two images. Second, we propose an “inverse prompt” solution to the corresponding prompt problem, by inverting Prompt X to the Image Y prompt space, where the Jacobian of prototypical features is used. Third, we propose a new registration algorithm that identifies multiple paired corresponding ROIs, by marginalizing the inverted Prompt X over both prompt and spatial spaces, random sampling Prompt X and spatial warping Image X. Comprehensive experiments were conducted on five applications of registering 3D prostate MR, 3D abdomen CT, 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 networks, even yielding competitive performance with weakly-supervised registration which requires fully-segmented training data.

External IDs:doi:10.1109/tip.2026.3683270