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Semi-automated quantitative Drosophila wings measurements

BACKGROUND: Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifyin...

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Autores principales: Loh, Sheng Yang Michael, Ogawa, Yoshitaka, Kawana, Sara, Tamura, Koichiro, Lee, Hwee Kuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490177/
https://www.ncbi.nlm.nih.gov/pubmed/28659123
http://dx.doi.org/10.1186/s12859-017-1720-y
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author Loh, Sheng Yang Michael
Ogawa, Yoshitaka
Kawana, Sara
Tamura, Koichiro
Lee, Hwee Kuan
author_facet Loh, Sheng Yang Michael
Ogawa, Yoshitaka
Kawana, Sara
Tamura, Koichiro
Lee, Hwee Kuan
author_sort Loh, Sheng Yang Michael
collection PubMed
description BACKGROUND: Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data. RESULTS: We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. CONCLUSION: Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1720-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-54901772017-06-30 Semi-automated quantitative Drosophila wings measurements Loh, Sheng Yang Michael Ogawa, Yoshitaka Kawana, Sara Tamura, Koichiro Lee, Hwee Kuan BMC Bioinformatics Software BACKGROUND: Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data. RESULTS: We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. CONCLUSION: Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1720-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-28 /pmc/articles/PMC5490177/ /pubmed/28659123 http://dx.doi.org/10.1186/s12859-017-1720-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://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/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Loh, Sheng Yang Michael
Ogawa, Yoshitaka
Kawana, Sara
Tamura, Koichiro
Lee, Hwee Kuan
Semi-automated quantitative Drosophila wings measurements
title Semi-automated quantitative Drosophila wings measurements
title_full Semi-automated quantitative Drosophila wings measurements
title_fullStr Semi-automated quantitative Drosophila wings measurements
title_full_unstemmed Semi-automated quantitative Drosophila wings measurements
title_short Semi-automated quantitative Drosophila wings measurements
title_sort semi-automated quantitative drosophila wings measurements
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490177/
https://www.ncbi.nlm.nih.gov/pubmed/28659123
http://dx.doi.org/10.1186/s12859-017-1720-y
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