ArticleIEEE transactions on medical imaging2020
ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- CurvReg: Curvature-aware registration for multi-stain alignment of liver biopsies.Journal of pathology informatics · 2026Article
- Joint zonated quantification of multiple parameters in hepatic lobules.Scientific reports · 2026Article
- SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.Briefings in bioinformatics · 2026Article
- MIAAIM: Multi-omics image integration with dimensional reduction for tissue state mapping.PLoS computational biology · 2026Article
- MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments.Communications biology · 2026Article
- Quantifying the Efficacy of Deep Learning-Driven Deformable Registration in Multiplexed-Immunofluorescence Imaging for Nucleus Subtype Classification.The journal of machine learning for biomedical imaging · 2026Article
- AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer.Nature medicine · 2026Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images.PLoS computational biology · 2025Article
- H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking.NPJ digital medicine · 2025Review
- ZeroReg3D: a zero-shot registration pipeline for 3D consecutive histopathology image reconstruction.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Reanalysis of Published Histological Data Can Help to Characterize Neuronal Death After Spinal Cord Injury.International journal of molecular sciences · 2025Article
- User-Assisted Approach for Accurate Nonrigid Registration of Images and Traces.bioRxiv : the preprint server for biology · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset.GigaScience · 2025Article
- An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Improving quality control of whole slide images by explicit artifact augmentation.Scientific reports · 2024Article
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29 authors.
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Abstract
Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.
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