Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-to-fine framework CORE for accurate nuclei-level registration across diverse multimodal whole-slide image (WSI) datasets. The coarse registration stage leverages prompt-based tissue mask extraction to effectively filter out artefacts and non-tissue regions, followed by global alignment using tissue morphology and ac- celerated dense feature matching with a pre-trained feature extractor. From the coarsely aligned slides, nuclei centroids are detected and subjected to fine-grained rigid registration using a custom, shape-aware point-set registration model. Finally, non-rigid alignment at the cellular level is achieved by estimating a non-linear dis- placement field using Coherent Point Drift (CPD). Our approach benefits from automatically generated nuclei that enhance the accuracy of deformable registra- tion and ensure precise nuclei-level correspondence across modalities. The pro- posed model is evaluated on three publicly available WSI registration datasets, and two private datasets. We show that CORE outperforms current state-of-the-art methods in terms of generalisability, precision, and robustness in bright-field and immunofluorescence microscopy WSIs
@article{arxiv.2511.03826,
title = {CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment},
author = {Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
journal= {arXiv preprint arXiv:2511.03826},
year = {2025}
}