The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated analysis of C. elegans imagery for the purpose of studying life-span, health-span and the underlying genetic determinants of aging. Our system detects and segments the worm, and predicts body coordinates at each pixel location inside the worm. These coordinates provide dense correspondence across individual animals to allow for meaningful comparative analysis. We show that a model pre-trained to perform body-coordinate regression extracts rich features that can be used to predict the age of individual worms with high accuracy. This lays the ground for future research in quantifying the relation between organs' physiologic and biochemical state, and individual life/health-span.
@article{arxiv.2005.04884,
title = {Celeganser: Automated Analysis of Nematode Morphology and Age},
author = {Linfeng Wang and Shu Kong and Zachary Pincus and Charless Fowlkes},
journal= {arXiv preprint arXiv:2005.04884},
year = {2020}
}
Comments
Computer Vision for Microscopy Image Analysis (CVMI) 2020