# From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review arXiv: https://arxiv.org/abs/2502.19123 ยท HTML: https://mlsanity.com/paper/2502.19123/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 158 references that are in the map. - First posted on arXiv 26 Feb 2025. ## Builds on (most cited first, 30 of 36) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [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) - [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018) - [Convolutional neural network architecture for geometric matching](https://mlsanity.com/paper/1703.05593/) (2017) - [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019) - [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019) - [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017) - [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) - [Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations](https://mlsanity.com/paper/1903.09331/) (2019) - [Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size](https://mlsanity.com/paper/1803.06629/) (2018) - [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022) - [Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN](https://mlsanity.com/paper/1809.04536/) (2018) - [Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration](https://mlsanity.com/paper/2007.02790/) (2020) - [Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge](https://mlsanity.com/paper/2309.15589/) (2023) - [Medical Image Registration Using Deep Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2002.03401/) (2020) - [Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues](https://mlsanity.com/paper/1705.08369/) (2017) - [Generative Adversarial Networks for MR-CT Deformable Image Registration](https://mlsanity.com/paper/1807.07349/) (2018) - [Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps](https://mlsanity.com/paper/2007.02078/) (2020) - [ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate](https://mlsanity.com/paper/2012.00991/) (2020) - [The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue](https://mlsanity.com/paper/2305.18033/) (2023) - [Robust, fast and accurate: a 3-step method for automatic histological image registration](https://mlsanity.com/paper/1903.12063/) (2019) - [Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors](https://mlsanity.com/paper/1907.13020/) (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) ## Built on by (most cited first, 2 of 2) - [CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment](https://mlsanity.com/paper/2511.03826/) (2025) - [STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images](https://mlsanity.com/paper/2509.02952/) (2025) ## Abstract (arXiv) Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.