Research Paper Volume 14, Issue 4 pp 1665—1677

A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility

Evgeniy Galimov1, *, , Artur Yakimovich1,2,3, *, ,

  • 1 Artificial Intelligence for Life Sciences CIC, London, United Kingdom
  • 2 Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR), Görlitz, Germany
  • 3 Bladder Infection and Immunity Group (BIIG), Department of Renal Medicine, Division of Medicine, University College London, Royal Free Hospital Campus, London, United Kingdom
* Equal contribution

Received: September 23, 2021       Accepted: February 18, 2022       Published: February 25, 2022
How to Cite

Copyright: © 2022 Galimov and Yakimovich. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


C. elegans is an established model organism for studying genetic and drug effects on aging, many of which are conserved in humans. It is also an important model for basic research, and C. elegans pathologies is a new emerging field. Here we develop a proof-of-principal convolutional neural network-based platform to segment C. elegans and extract features that might be useful for lifespan prediction. We use a dataset of 734 worms tracked throughout their lifespan and classify worms into long-lived and short-lived. We designed WormNet - a convolutional neural network (CNN) to predict the worm lifespan class based on young adult images (day 1 – day 3 old adults) and showed that WormNet, as well as, InceptionV3 CNN can successfully classify lifespan. Based on U-Net architecture we develop HydraNet CNNs which allow segmenting worms accurately into anterior, mid-body and posterior parts. We combine HydraNet segmentation, WormNet prediction and the class activation map approach to determine the segments most important for lifespan classification. Such a tandem segmentation-classification approach shows the posterior part of the worm might be more important for classifying long-lived worms. Our approach can be useful for the acceleration of anti-aging drug discovery and for studying C. elegans pathologies.


AUC ROC: area under the curve receiver operating characteristic; CAM: class activation map; CNN: convolutional neural network; MAE: mean absolute error; ReLU: rectified linear unit.