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Multi-template matching: a versatile tool for object-localization in microscopy images

BACKGROUND: The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching...

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Autores principales: Thomas, Laurent S. V., Gehrig, Jochen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003318/
https://www.ncbi.nlm.nih.gov/pubmed/32024462
http://dx.doi.org/10.1186/s12859-020-3363-7
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author Thomas, Laurent S. V.
Gehrig, Jochen
author_facet Thomas, Laurent S. V.
Gehrig, Jochen
author_sort Thomas, Laurent S. V.
collection PubMed
description BACKGROUND: The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity. RESULTS: We provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github.com/multi-template-matching). CONCLUSION: The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.
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spelling pubmed-70033182020-02-10 Multi-template matching: a versatile tool for object-localization in microscopy images Thomas, Laurent S. V. Gehrig, Jochen BMC Bioinformatics Software BACKGROUND: The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity. RESULTS: We provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github.com/multi-template-matching). CONCLUSION: The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions. BioMed Central 2020-02-05 /pmc/articles/PMC7003318/ /pubmed/32024462 http://dx.doi.org/10.1186/s12859-020-3363-7 Text en © The Author(s). 2020, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Thomas, Laurent S. V.
Gehrig, Jochen
Multi-template matching: a versatile tool for object-localization in microscopy images
title Multi-template matching: a versatile tool for object-localization in microscopy images
title_full Multi-template matching: a versatile tool for object-localization in microscopy images
title_fullStr Multi-template matching: a versatile tool for object-localization in microscopy images
title_full_unstemmed Multi-template matching: a versatile tool for object-localization in microscopy images
title_short Multi-template matching: a versatile tool for object-localization in microscopy images
title_sort multi-template matching: a versatile tool for object-localization in microscopy images
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003318/
https://www.ncbi.nlm.nih.gov/pubmed/32024462
http://dx.doi.org/10.1186/s12859-020-3363-7
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