# Automatic Multi-Stain Registration of Whole Slide Images in Histopathology

arXiv: https://arxiv.org/abs/2107.14292 · HTML: https://mlsanity.com/paper/2107.14292/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides](https://mlsanity.com/paper/2101.07903/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks](https://mlsanity.com/paper/2308.11162/) (2023)
- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Immunohistochemistry Biomarkers-Guided Image Search for Histopathology](https://mlsanity.com/paper/2304.12424/) (2023)
- [Composite Biomarker Image for Advanced Visualization in Histopathology](https://mlsanity.com/paper/2304.12423/) (2023)
- [Deep Learning for Generating Computational PIN-4 Immunohistochemistry Staining from Prostate Biopsy H&E Images](https://mlsanity.com/paper/2606.01871/) (2026)

## Abstract (arXiv)

Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immuno-histochemical and hematoxylin and eosin (H&E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.