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

arXiv: https://arxiv.org/abs/2511.03826 · HTML: https://mlsanity.com/paper/2511.03826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [XFeat: Accelerated Features for Lightweight Image Matching](https://mlsanity.com/paper/2404.19174/) (2024)
- [Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology](https://mlsanity.com/paper/2106.13150/) (2021)
- [Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps](https://mlsanity.com/paper/2007.02078/) (2020)
- [Robust, fast and accurate: a 3-step method for automatic histological image registration](https://mlsanity.com/paper/1903.12063/) (2019)
- [Deep Feature based Cross-slide Registration](https://mlsanity.com/paper/2202.09971/) (2022)
- [RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge](https://mlsanity.com/paper/2404.13108/) (2024)
- [Nuclei-Location Based Point Set Registration of Multi-Stained Whole Slide Images](https://mlsanity.com/paper/2404.17041/) (2024)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis](https://mlsanity.com/paper/2410.00152/) (2024)

## Built on by (most cited first, 1 of 1)

- [Vision-Language Models as Zero-Annotation Oracles in Histopathology](https://mlsanity.com/paper/2606.16658/) (2026)

## Abstract (arXiv)

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