ArticleZoological research2021
3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis.
Article in Zoological research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Bilateral asymmetry in craniofacial structures and kinematics of feeding attacks in the scale-eating cichlid fish,Zoological research · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Population structure and adaptability analysis of Schizothorax o'connori based on whole-genome resequencing.BMC genomics · 2024Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- Phenotypic variation ofPeerJ · 2022Article
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Authors and funding
14 authors.
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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
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