WormTracer: A precise method for worm posture analysis using temporal continuity
Koyo Kuze, Ukyo T. TazawaiD, Karin SuwazonoiD, Chung-Kuan CheniD, Yu ToyoshimaiD, Yuichi IinoiD
Abstract
This study introduces WormTracer, a novel algorithm designed to accurately quantify temporal evolution of worm postures. Unlike conventional methods that analyze individual images separately, WormTracer estimates worm centerlines within a sequence of images concurrently. This process enables the resolution of complex postures that are difficult to assess when treated as isolated images. The centerlines obtained through WormTracer exhibit higher accuracy compared to those acquired using conventional methods. By applying principal component analysis to the centerlines obtained by WormTracer, we successfully generated new eigenworms, a basic set of postures, that enables a more precise representation of worm postures than existing eigenworms. Author summaryC. elegans is a valuable model organism for comprehensive understanding of genes, neurons and behavior. Quantification of behavior is essential for clarifying these relationships, and posture information plays a crucial role in the analyses. However, accurately quantifying the posture of C. elegans from video images of worms is challenging, and while various methods have been developed to date, they have their own limitations. In this study, we developed an analytical tool called WormTracer, which can obtain worm centerlines more accurately than conventional methods, even when worms assume complex postures. Using this tool, we successfully obtained new eigenworms, basis postures of a worm, that can more accurately reproduce various postures than conventional eigenworms. WormTracer and the new eigenworms will be valuable assets for future quantitative studies on worm locomotion and sensorimotor behaviors.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 5
- FWCI
- 1.88
- Field percentile
- top 13%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Deep-Pose-Tracker: an automated behavioural analysis framework for Caenorhabditis elegans
Debasish Saha, Shivam Chaudhary, … Rati Ram SHARMA·Scientific ReportsPeer-reviewed·Aug 6, 2026
- A MULTI-PURPOSE WORM TRACKER BASED ON FIM
Matthias Kiel, Dimitri Berh, … Benjamin Risse·bioRxiv·Jun 21, 2018
- An improved neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens
Weheliye H. Weheliye, Javier Rodríguez, … André EX Brown·PLoS Computational BiologyPeer-reviewed·Aug 8, 2025
- WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
L. Hebert, T. Ahamed, … G. J. Stephens·bioRxiv·Jul 9, 2020
- Unsupervised Representation Learning of C. elegans Poses and Behavior Sequences From Microscope Video Recordings
Maurice Deserno, Katarzyna Bożek·bioRxiv·Feb 14, 2025
- Track-A-Worm 2.0: A Software Suite for Quantifying Properties of C. elegans Locomotion, Bending, Sleep, and Action Potentials
Kiranmayi Vedantham, Longgang Niu, … Zhao‐Wen Wang·eNeuroPeer-reviewed·Aug 1, 2025