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Automatic Nonrigid Histological Image Registration with Adaptive Multistep Algorithm
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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.
Forward citations
Cited by 2 Pith papers
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MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
The authors show that pre-training detector-free matchers on 800 million diverse image pairs, including synthetic thermal, night, and depth views, gives one network that generalizes to unseen cross-modal matching tasks.
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STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images
STAR aligns multi-stain serial whole-slide pathology images through hierarchical rotation-translation correlation, with stain-conditioned preprocessing and quality control.
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