Automatic Nonrigid Histological Image Registration with Adaptive Multistep Algorithm
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Abstract (arXiv)
In this paper, we present a short description of the method proposed to ANHIR challenge organized jointly with the IEEE ISBI 2019 conference. We propose a method consisting of preprocessing, initial alignment, nonrigid registration algorithms and a method to automatically choose the best result. The method turned out to be robust (99.792% robustness) and accurate (0.38% average median rTRE). The main drawback of the proposed method is relatively high computation time. However, this aspect can be easily improved by cleaning the code and proposing a GPU implementation.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Apr 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.
3 papers.
- 2025MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Trainingcited by 41
- 2022A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Imagescited by 0
- 2025STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Imagescited by 0
- 2025STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images3 Sep 2025
- 2025MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training13 Jan 2025
- 2022A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images16 Aug 2022