# Robust, fast and accurate: a 3-step method for automatic histological image registration

arXiv: https://arxiv.org/abs/1903.12063 · HTML: https://mlsanity.com/paper/1903.12063/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training](https://mlsanity.com/paper/2501.07556/) (2025)
- [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)
- [AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer](https://mlsanity.com/paper/2309.06421/) (2023)
- [CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment](https://mlsanity.com/paper/2511.03826/) (2025)
- [A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images](https://mlsanity.com/paper/2208.07655/) (2022)
- [UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift](https://mlsanity.com/paper/2503.20653/) (2025)

## Abstract (arXiv)

We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF distance measure is minimized with respect to an increasingly flexible transformation. We apply the method in the ANHIR image registration challenge and evaluate its performance on the training data. The presented method is robust (error reduction in 99.6% of the cases), fast (runtime 4 seconds) and accurate (median relative target registration error 0.19%).