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

文献类型： 外文期刊

作者： Liao, Yu-Hang 1 ; Zhou, Chao-Wei 2 ; Liu, Wei-Zhen 1 ; Jin, Jing-Yi 4 ; Li, Dong-Ye 4 ; Liu, Fei 2 ; Fan, Ding-Ding 2 ; Z 1 ;

作者机构： 1.Wuhan Univ Technol, Dept Comp Sci, Wuhan 430070, Hubei, Peoples R China

2.Tibet Acad Agr & Anim Husb Sci, Inst Fisheries Sci, Lhasa 850000, Tibet, Peoples R China

3.Southwest Univ, Coll Fisheries, Key Lab Freshwater Fish Reprod & Dev, Minist Educ, Chongqing 402460, Peoples R China

4.Jiaxing Key Lab New Germplasm Breeding Econ Mycol, Jiaxing 314000, Zhejiang, Peoples R China

5.Huadian Tibet Energy Co Ltd, Lhasa 851415, Tibet, Peoples R China

6.China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China

关键词： Fish; Phenomics; Morphology; Point cloud; 3D scanning

期刊名称：ZOOLOGICAL RESEARCH （ 影响因子：4.56； ）

ISSN： 2095-8137

年卷期： 2021 年 42 卷 4 期

页码：

收录情况： SCI

摘要： 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.

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