# Deep Feature based Cross-slide Registration

arXiv: https://arxiv.org/abs/2202.09971 · HTML: https://mlsanity.com/paper/2202.09971/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks](https://mlsanity.com/paper/1808.05896/) (2018)
- [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)
- [Whole slide image registration for the study of tumor heterogeneity](https://mlsanity.com/paper/1901.08317/) (2019)

## Built on by (most cited first, 7 of 7)

- [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)
- [DeeperHistReg: Robust Whole Slide Images Registration Framework](https://mlsanity.com/paper/2404.14434/) (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)
- [CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment](https://mlsanity.com/paper/2511.03826/) (2025)
- [Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer](https://mlsanity.com/paper/2203.00449/) (2022)
- [Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge](https://mlsanity.com/paper/2409.15931/) (2024)

## Abstract (arXiv)

Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slides are analysed side by side, revealing unknown relations between them. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each section to a reference section of the same tissue block an important pre-requisite task before any cross-slide analysis. We propose a deep feature based registration (DFBR) method which utilises data-driven features to estimate the rigid transformation. We adopted a multi-stage strategy for improving the quality of registration. We also developed a visualisation tool to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate transformed source WSI in a pyramidal form. We compared the performance of data-driven features with that of hand-crafted features on the COMET dataset. Our approach can align the images with low registration errors. Generally, the success of non-rigid registration is dependent on the quality of rigid registration. To evaluate the efficacy of the DFBR method, the first two steps of the ANHIR winner's framework are replaced with our DFBR to register challenge provided image pairs. The modified framework produces comparable results to that of challenge winning team.