AI Insight
This study presents CORE, a new computational framework for aligning whole slide images of tissue samples stained with different methods. The system works through a multi-stage process: first performing global alignment using tissue structure, then detecting cell nuclei and aligning them precisely, and finally applying non-rigid adjustments at the cellular level using Coherent Point Drift algorithms. When tested on five datasets including both public and private collections, CORE demonstrated superior performance compared to existing methods in accurately matching nuclei-level features across different imaging modalities.
Why it matters
Accurate alignment of multi-stained tissue images is critical for cancer research and diagnostic pathology, where comparing the same tissue section under different staining techniques reveals complementary biological information. This improved registration method could enhance digital pathology workflows and enable more precise automated analysis of tissue samples at the individual cell level.
Understand the Science
arXiv:2511.03826v4 Announce Type: replace
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 accelerated 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 displacement field using Coherent Point Drift (CPD). Our approach benefits from automatically generated nuclei that enhance the accuracy of deformable registration and ensure precise nuclei-level correspondence across modalities. The proposed 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
Source: CORE — A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment