Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783381278735728640 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6298256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rosenhainstefanie apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT magnuskazuzannaa apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT yamoahgraceg apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT rawashdehwaelal apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT kiesslingfabian apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT gremsefelix apreclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT rosenhainstefanie preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT magnuskazuzannaa preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT yamoahgraceg preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT rawashdehwaelal preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT kiesslingfabian preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations AT gremsefelix preclinicalmicrocomputedtomographydatabaseincluding3dwholebodyorgansegmentations |