CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
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

TL;DR
CORE introduces a multi-stage, cell-level image registration framework that combines coarse tissue alignment with fine nuclei matching and non-rigid cellular registration, improving accuracy and robustness across diverse multi-stain whole-slide images.
Contribution
The paper presents a novel coarse-to-fine registration method utilizing prompt-based tissue masks and shape-aware point-set registration for nuclei-level alignment in multi-stain WSIs.
Findings
Outperforms state-of-the-art methods in accuracy and robustness.
Effective across multiple modalities and datasets.
Enhances nuclei correspondence precision.
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…
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Taxonomy
TopicsMedical Image Segmentation Techniques · AI in cancer detection · Cell Image Analysis Techniques
