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廖宇航, 周朝伟, 刘维真, 金靖熠, 李东野, 刘飞, 范丁丁, 邹禹, 牟振波, 沈建, 刘春娜, 肖世俊, 袁晓辉, 刘海平. 2021: 3DPhenoFish: 基于点云分析的二维和三维鱼类形态表型分析和应用. 动物学研究, 42(4): 492-502. DOI: 10.24272/j.issn.2095-8137.2021.141
引用本文: 廖宇航, 周朝伟, 刘维真, 金靖熠, 李东野, 刘飞, 范丁丁, 邹禹, 牟振波, 沈建, 刘春娜, 肖世俊, 袁晓辉, 刘海平. 2021: 3DPhenoFish: 基于点云分析的二维和三维鱼类形态表型分析和应用. 动物学研究, 42(4): 492-502. DOI: 10.24272/j.issn.2095-8137.2021.141
Yu-Hang Liao, Chao-Wei Zhou, Wei-Zhen Liu, Jing-Yi Jin, Dong-Ye Li, Fei Liu, Ding-Ding Fan, Yu Zou, Zen-Bo Mu, Jian Shen, Chun-Na Liu, Shi-Jun Xiao, Xiao-Hui Yuan, Hai-Ping Liu. 2021: 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis. Zoological Research, 42(4): 492-502. DOI: 10.24272/j.issn.2095-8137.2021.141
Citation: Yu-Hang Liao, Chao-Wei Zhou, Wei-Zhen Liu, Jing-Yi Jin, Dong-Ye Li, Fei Liu, Ding-Ding Fan, Yu Zou, Zen-Bo Mu, Jian Shen, Chun-Na Liu, Shi-Jun Xiao, Xiao-Hui Yuan, Hai-Ping Liu. 2021: 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis. Zoological Research, 42(4): 492-502. DOI: 10.24272/j.issn.2095-8137.2021.141

3DPhenoFish: 基于点云分析的二维和三维鱼类形态表型分析和应用

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

  • 摘要: 鱼类形态表型是水产养殖和生态学研究中人工育种、功能基因定位和群体遗传分析的重要资源。传统的形态学表型测量需要耗费大量的时间与劳动力,更重要的是人工测量高度依赖于操作经验,导致表型测量的结果具有一定的主观性。因此,我们开发了一个可以从三维点云数据中提取鱼类形态表型的软件3DPhenoFish。该软件提供了一个直观的用户界面,将背景剔除、坐标归一化、三维分割、关键点识别和表型提取的功能进行了整合。用户可以自动获取鱼体上18个关键形态点,基于关键点的二维表型,以及鱼体表面积和体积的三维表型。同时,3DPhenoFish还允许用户为自动识别的关键点进行微调,并自定义个性化的表型。基于3DPhenoFish,我们对四种高原特有的裂腹鱼亚科鱼类进行了高通量表型分析,包括拉萨裸裂尻、拉萨河尖裸鲤、双须叶须鱼和异齿裂腹鱼。结果表明,使用3DPhenoFish高通量提取的形态表型与人工测量结果表现出高度的线性相关性(>0.94)。基于高通量提取的形态学表型,我们可以很好地将不同物种进行区分,甚至可以区分同一物种的不同种群。综上所述,我们开发了高效、准确和可定制的鱼类表型分析工具3DPhenoFish,用于从三维点云数据中批量提取形态学表型,有助于克服人工测量中的低通量和高成本的一些传统挑战,因此3DPhenoFish可用于功能基因定位、新品种培育和资源保护研究中的表型分析。3DPhenoFish是一个开源软件,并可以在https://github.com/lyh24k/3DPhenoFish/tree/master免费下载。

     

    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.

     

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