High-throughput Screening with Deep Learning for Quantitative Phenotype Analysis of Zebrafish

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.