CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911285946351616 |
|---|---|
| author | Nasir, Esha Sadia Elhaminia, Behnaz Eastwood, Mark King, Catherine Cain, Owen Harper, Lorraine Moss, Paul Chanouzas, Dimitrios Snead, David Rajpoot, Nasir Shephard, Adam Raza, Shan E Ahmed |
| author_facet | Nasir, Esha Sadia Elhaminia, Behnaz Eastwood, Mark King, Catherine Cain, Owen Harper, Lorraine Moss, Paul Chanouzas, Dimitrios Snead, David Rajpoot, Nasir Shephard, Adam Raza, Shan E Ahmed |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03826 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment Nasir, Esha Sadia Elhaminia, Behnaz Eastwood, Mark King, Catherine Cain, Owen Harper, Lorraine Moss, Paul Chanouzas, Dimitrios Snead, David Rajpoot, Nasir Shephard, Adam Raza, Shan E Ahmed Quantitative Methods Artificial Intelligence 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 |
| title | CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2511.03826 |