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3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis

Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is...

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Autores principales: Liao, Yu-Hang, Zhou, Chao-Wei, Liu, Wei-Zhen, Jin, Jing-Yi, Li, Dong-Ye, Liu, Fei, Fan, Ding-Ding, Zou, Yu, Mu, Zen-Bo, Shen, Jian, Liu, Chun-Na, Xiao, Shi-Jun, Yuan, Xiao-Hui, Liu, Hai-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Science Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317184/
https://www.ncbi.nlm.nih.gov/pubmed/34235898
http://dx.doi.org/10.24272/j.issn.2095-8137.2021.141
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author Liao, Yu-Hang
Zhou, Chao-Wei
Liu, Wei-Zhen
Jin, Jing-Yi
Li, Dong-Ye
Liu, Fei
Fan, Ding-Ding
Zou, Yu
Mu, Zen-Bo
Shen, Jian
Liu, Chun-Na
Xiao, Shi-Jun
Yuan, Xiao-Hui
Liu, Hai-Ping
author_facet Liao, Yu-Hang
Zhou, Chao-Wei
Liu, Wei-Zhen
Jin, Jing-Yi
Li, Dong-Ye
Liu, Fei
Fan, Ding-Ding
Zou, Yu
Mu, Zen-Bo
Shen, Jian
Liu, Chun-Na
Xiao, Shi-Jun
Yuan, Xiao-Hui
Liu, Hai-Ping
author_sort Liao, Yu-Hang
collection PubMed
description Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is highly dependent on operational experience, which can lead to subjective phenotyping results. Here, we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional (3D) point cloud data. Algorithms for background elimination, coordinate normalization, image segmentation, key point recognition, and phenotype extraction were developed and integrated into an intuitive user interface. Furthermore, 18 key points and traditional 2D morphological traits, along with 3D phenotypes, including area and volume, can be automatically obtained in a visualized manner. Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software. Using 3DPhenoFish, we performed high-throughput phenotyping for four endemic Schizothoracinae species, including Schizopygopsis younghusbandi, Oxygymnocypris stewartii, Ptychobarbus dipogon, and Schizothorax oconnori. Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation (>0.94) with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species. In summary, we developed an efficient, accurate, and customizable tool, 3DPhenoFish, to extract morphological phenotypes from point cloud data, which should help overcome traditional challenges in manual measurements. 3DPhenoFish can be used for research on morphological phenotypes in fish, including functional gene mapping, artificial selection, and conservation studies. 3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.
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spelling pubmed-83171842021-07-30 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis Liao, Yu-Hang Zhou, Chao-Wei Liu, Wei-Zhen Jin, Jing-Yi Li, Dong-Ye Liu, Fei Fan, Ding-Ding Zou, Yu Mu, Zen-Bo Shen, Jian Liu, Chun-Na Xiao, Shi-Jun Yuan, Xiao-Hui Liu, Hai-Ping Zool Res Article Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is highly dependent on operational experience, which can lead to subjective phenotyping results. Here, we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional (3D) point cloud data. Algorithms for background elimination, coordinate normalization, image segmentation, key point recognition, and phenotype extraction were developed and integrated into an intuitive user interface. Furthermore, 18 key points and traditional 2D morphological traits, along with 3D phenotypes, including area and volume, can be automatically obtained in a visualized manner. Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software. Using 3DPhenoFish, we performed high-throughput phenotyping for four endemic Schizothoracinae species, including Schizopygopsis younghusbandi, Oxygymnocypris stewartii, Ptychobarbus dipogon, and Schizothorax oconnori. Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation (>0.94) with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species. In summary, we developed an efficient, accurate, and customizable tool, 3DPhenoFish, to extract morphological phenotypes from point cloud data, which should help overcome traditional challenges in manual measurements. 3DPhenoFish can be used for research on morphological phenotypes in fish, including functional gene mapping, artificial selection, and conservation studies. 3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master. Science Press 2021-07-18 /pmc/articles/PMC8317184/ /pubmed/34235898 http://dx.doi.org/10.24272/j.issn.2095-8137.2021.141 Text en Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Liao, Yu-Hang
Zhou, Chao-Wei
Liu, Wei-Zhen
Jin, Jing-Yi
Li, Dong-Ye
Liu, Fei
Fan, Ding-Ding
Zou, Yu
Mu, Zen-Bo
Shen, Jian
Liu, Chun-Na
Xiao, Shi-Jun
Yuan, Xiao-Hui
Liu, Hai-Ping
3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title_full 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title_fullStr 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title_full_unstemmed 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title_short 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
title_sort 3dphenofish: application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317184/
https://www.ncbi.nlm.nih.gov/pubmed/34235898
http://dx.doi.org/10.24272/j.issn.2095-8137.2021.141
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