2023/12/02 by Avasthi, Prachee, Celebi, Feridun Mert, Cheveralls, Keith +3
#FOS: Biological sciences
paper · doi:10.57844/arcadia-c021-e384
We’re broadly interested in extracting biological function from label-free, high-throughput imaging data. As a first pass, we tested the effectiveness of a deep-learning framework that incorporates temporal information in classifying the developmental stage of the well-studied nematode, Caenorhabditis elegans. We trained a classifier that you can use to identify nematode embryo stages from time-course datasets captured using bright-field microscopy. We hope this tool will be immediately useful to interrogate embryonic development, reproductive success, or developmental outcomes following perturbations in C. elegans or other free-living nematode species. More broadly, you can adapt our approach to any category of classifiable microscopy time-course data. To this end, we provide a PyTorch-based pipeline for training and evaluating your own models. The tool lets you go from imaging nematode embryos to classifying developmental stages and quantifying the frequency of successful versus unsuccessful developmental outcomes. It’s about 80% accurate in calling the correct stage. We’re not pursuing this project further but welcome your input and encourage others to incorporate user feedback to improve the functionality of the classifier if it’s useful to you.