Literature DB >> 34235898

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

Yu-Hang Liao1, Chao-Wei Zhou2,3, Wei-Zhen Liu1, Jing-Yi Jin4, Dong-Ye Li4, Fei Liu2, Ding-Ding Fan2, Yu Zou4, Zen-Bo Mu2, Jian Shen5, Chun-Na Liu6, Shi-Jun Xiao1,2,7, Xiao-Hui Yuan8, Hai-Ping Liu9.   

Abstract

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.

Entities:  

Keywords:  3D scanning; Fish; Morphology; Phenomics; Point cloud

Mesh:

Year:  2021        PMID: 34235898      PMCID: PMC8317184          DOI: 10.24272/j.issn.2095-8137.2021.141

Source DB:  PubMed          Journal:  Zool Res        ISSN: 2095-8137


  6 in total

1.  HTPheno: an image analysis pipeline for high-throughput plant phenotyping.

Authors:  Anja Hartmann; Tobias Czauderna; Roberto Hoffmann; Nils Stein; Falk Schreiber
Journal:  BMC Bioinformatics       Date:  2011-05-12       Impact factor: 3.169

2.  On-Tree Mango Fruit Size Estimation Using RGB-D Images.

Authors:  Zhenglin Wang; Kerry B Walsh; Brijesh Verma
Journal:  Sensors (Basel)       Date:  2017-11-28       Impact factor: 3.576

3.  A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns.

Authors:  Andrea Pezzuolo; Marcella Guarino; Luigi Sartori; Francesco Marinello
Journal:  Sensors (Basel)       Date:  2018-02-24       Impact factor: 3.576

4.  Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor.

Authors:  Lvwen Huang; Shuqin Li; Anqi Zhu; Xinyun Fan; Chenyang Zhang; Hongyan Wang
Journal:  Sensors (Basel)       Date:  2018-09-09       Impact factor: 3.576

5.  Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field.

Authors:  Guichao Lin; Yunchao Tang; Xiangjun Zou; Juntao Xiong; Jinhui Li
Journal:  Sensors (Basel)       Date:  2019-01-21       Impact factor: 3.576

6.  Fish-Pak: Fish species dataset from Pakistan for visual features based classification.

Authors:  Syed Zakir Hussain Shah; Hafiz Tayyab Rauf; Muhammad IkramUllah; Malik Shahzaib Khalid; Muhammad Farooq; Mahroze Fatima; Syed Ahmad Chan Bukhari
Journal:  Data Brief       Date:  2019-10-04
  6 in total
  1 in total

1.  Phenotypic variation of Chitala chitala (Hamilton, 1822) from Indian rivers using truss network and geometric morphometrics.

Authors:  Rejani Chandran; Achal Singh; Rajeev K Singh; Sangeeta Mandal; Kantharajan Ganesan; Priyanka Sah; Pradipta Paul; Abhinav Pathak; Nimisha Dutta; Ramashankar Sah; Kuldeep K Lal; Vindhya Mohindra
Journal:  PeerJ       Date:  2022-04-18       Impact factor: 3.061

  1 in total

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