  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.
