DeeperHistReg: Robust Whole Slide Images Registration Framework

Fuente: arXiv
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Autores principales: Wodzinski, Marek, Marini, Niccolò, Atzori, Manfredo, Müller, Henning
Formato: Preprint
Publicado: 2024
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author Wodzinski, Marek
Marini, Niccolò
Atzori, Manfredo
Müller, Henning
author_facet Wodzinski, Marek
Marini, Niccolò
Atzori, Manfredo
Müller, Henning
contents DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains. It allows one to perform the preprocessing, initial alignment, and nonrigid registration of WSIs acquired using multiple stains (e.g. hematoxylin \& eosin, immunochemistry). The framework implements several state-of-the-art registration algorithms and provides an interface to operate on arbitrary resolution of the WSIs (up to 200k x 200k). The framework is extensible and new algorithms can be easily integrated by other researchers. The framework is available both as a PyPI package and as a Docker container.
format Preprint
id arxiv_https___arxiv_org_abs_2404_14434
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle DeeperHistReg: Robust Whole Slide Images Registration Framework
Wodzinski, Marek
Marini, Niccolò
Atzori, Manfredo
Müller, Henning
Image and Video Processing
Computer Vision and Pattern Recognition
Quantitative Methods
DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains. It allows one to perform the preprocessing, initial alignment, and nonrigid registration of WSIs acquired using multiple stains (e.g. hematoxylin \& eosin, immunochemistry). The framework implements several state-of-the-art registration algorithms and provides an interface to operate on arbitrary resolution of the WSIs (up to 200k x 200k). The framework is extensible and new algorithms can be easily integrated by other researchers. The framework is available both as a PyPI package and as a Docker container.
title DeeperHistReg: Robust Whole Slide Images Registration Framework
topic Image and Video Processing
Computer Vision and Pattern Recognition
Quantitative Methods
url https://arxiv.org/abs/2404.14434