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A preclinical micro-computed tomography database including 3D whole body organ segmentations

The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Un...

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Autores principales: Rosenhain, Stefanie, Magnuska, Zuzanna A., Yamoah, Grace G., Rawashdeh, Wa’el Al, Kiessling, Fabian, Gremse, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298256/
https://www.ncbi.nlm.nih.gov/pubmed/30561432
http://dx.doi.org/10.1038/sdata.2018.294
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author Rosenhain, Stefanie
Magnuska, Zuzanna A.
Yamoah, Grace G.
Rawashdeh, Wa’el Al
Kiessling, Fabian
Gremse, Felix
author_facet Rosenhain, Stefanie
Magnuska, Zuzanna A.
Yamoah, Grace G.
Rawashdeh, Wa’el Al
Kiessling, Fabian
Gremse, Felix
author_sort Rosenhain, Stefanie
collection PubMed
description The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Unfortunately, robust and accurate automated whole body segmentation algorithms are still missing. In this publication, we introduce a database containing 225 murine 3D whole body μCT scans along with manual organ segmentation of most important organs including heart, liver, lung, trachea, spleen, kidneys, stomach, intestine, bladder, thigh muscle, bone, as well as subcutaneous tumors. The database includes native and contrast-enhanced, regarding spleen and liver, μCT data. All scans along with organ segmentation are freely accessible at the online repository Figshare. We encourage researchers to reuse the provided data to evaluate and improve methods and algorithms for accurate automated organ segmentation which may reduce manual segmentation effort, increase reproducibility, and even reduce the number of required laboratory animals by reducing a source of variability and having access to a reliable reference group.
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spelling pubmed-62982562018-12-19 A preclinical micro-computed tomography database including 3D whole body organ segmentations Rosenhain, Stefanie Magnuska, Zuzanna A. Yamoah, Grace G. Rawashdeh, Wa’el Al Kiessling, Fabian Gremse, Felix Sci Data Data Descriptor The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Unfortunately, robust and accurate automated whole body segmentation algorithms are still missing. In this publication, we introduce a database containing 225 murine 3D whole body μCT scans along with manual organ segmentation of most important organs including heart, liver, lung, trachea, spleen, kidneys, stomach, intestine, bladder, thigh muscle, bone, as well as subcutaneous tumors. The database includes native and contrast-enhanced, regarding spleen and liver, μCT data. All scans along with organ segmentation are freely accessible at the online repository Figshare. We encourage researchers to reuse the provided data to evaluate and improve methods and algorithms for accurate automated organ segmentation which may reduce manual segmentation effort, increase reproducibility, and even reduce the number of required laboratory animals by reducing a source of variability and having access to a reliable reference group. Nature Publishing Group 2018-12-18 /pmc/articles/PMC6298256/ /pubmed/30561432 http://dx.doi.org/10.1038/sdata.2018.294 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Rosenhain, Stefanie
Magnuska, Zuzanna A.
Yamoah, Grace G.
Rawashdeh, Wa’el Al
Kiessling, Fabian
Gremse, Felix
A preclinical micro-computed tomography database including 3D whole body organ segmentations
title A preclinical micro-computed tomography database including 3D whole body organ segmentations
title_full A preclinical micro-computed tomography database including 3D whole body organ segmentations
title_fullStr A preclinical micro-computed tomography database including 3D whole body organ segmentations
title_full_unstemmed A preclinical micro-computed tomography database including 3D whole body organ segmentations
title_short A preclinical micro-computed tomography database including 3D whole body organ segmentations
title_sort preclinical micro-computed tomography database including 3d whole body organ segmentations
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298256/
https://www.ncbi.nlm.nih.gov/pubmed/30561432
http://dx.doi.org/10.1038/sdata.2018.294
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