High-throughput screening with deep learning for quantitative phenotype analysis of zebrafish

Geoseong Na; Hyunmo Yang; Unbeom Shin; Yerim Kim; Sanzhar Askaruly; Taejoon Kwon; Yoonsung Lee; Woonggyu Jung

doi:10.1117/12.2610138

2 March 2022 High-throughput screening with deep learning for quantitative phenotype analysis of zebrafish

Geoseong Na, Hyunmo Yang, Unbeom Shin, Yerim Kim, Sanzhar Askaruly, Taejoon Kwon, Yoonsung Lee, Woonggyu Jung

Proceedings Volume PC11971, High-Speed Biomedical Imaging and Spectroscopy VII; PC119710C (2022) https://doi.org/10.1117/12.2610138
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Zebrafish is a useful biological model for analyzing genetic modification and large-scale screening. Its morphological evaluation, carrying meaningful information about genotype-phenotype relationship, is equally important. However, analysis of large amounts across development stages is a labor-intensive task. Here, we suggest a high-throughput monitoring technique using office scanner. Moreover, we developed deep learning models for extraction and analysis of massive statistical information. CNN-based architecture, forming the core of segmentation, serves as a basis for quantitative analysis and an early signal for embryo’s abnormal growth. Finally, compared to conventional microscope imaging, our scanning technique offers high-throughput, accurate, and fast quantitative phenotype analysis.

Conference Presentation

Citation Download Citation

Geoseong Na, Hyunmo Yang, Unbeom Shin, Yerim Kim, Sanzhar Askaruly, Taejoon Kwon, Yoonsung Lee, and Woonggyu Jung "High-throughput screening with deep learning for quantitative phenotype analysis of zebrafish", Proc. SPIE PC11971, High-Speed Biomedical Imaging and Spectroscopy VII, PC119710C (2 March 2022); https://doi.org/10.1117/12.2610138

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