{"ID":2845947,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03826","arxiv_id":"2511.03826","title":"CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment","abstract":"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","short_abstract":"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 registra...","url_abs":"https://arxiv.org/abs/2511.03826","url_pdf":"https://arxiv.org/pdf/2511.03826v3","authors":"[\"Esha Sadia Nasir\",\"Behnaz Elhaminia\",\"Mark Eastwood\",\"Catherine King\",\"Owen Cain\",\"Lorraine Harper\",\"Paul Moss\",\"Dimitrios Chanouzas\",\"David Snead\",\"Nasir Rajpoot\",\"Adam Shephard\",\"Shan E Ahmed Raza\"]","published":"2025-11-05T19:47:41Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.AI\"]","methods":"[]","has_code":false}