 Automated C. elegans behavior analysis via deep learning-based detection and trackingXiaoke Liu, Jianming Liu, Wenjie Teng, Yuzhong Peng, Boao Li, Xiaoqing Han and Jing Huo PLOS Computational Biology, 2025, vol. 21, issue 11, 1-26 Abstract: As a well-established and extensively utilized model organism, Caenorhabditis elegans (C. elegans) serves as a crucial platform for investigating behavioral regulation mechanisms and their biological significance. However, manually tracking the locomotor behavior of large numbers of C. elegans is both cumbersome and inefficient. To address the above challenges, we innovatively propose an automated approach for analyzing C. elegans behavior through deep learning-based detection and tracking. Building upon existing research, we developed an enhanced worm detection framework that integrates YOLOv8 with ByteTrack, enabling real-time, precise tracking of multiple worms. Based on the tracking results, we further established an automated high-throughput method for quantitative analysis of multiple movement parameters, including locomotion velocity, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. including movement speed, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. Comparative evaluations demonstrate that the proposed enhanced C. elegans detection framework outperforms existing methods, achieving a precision of 99.5%, recall of 98.7%, and mAP50 of 99.6%, with a processing speed of 153 frames per second (FPS). The established framework for worm detection, tracking, and automated behavioral analysis developed in this study delivers superior detection and tracking accuracy while enhancing tracking continuity and robustness. Unlike traditional labor-intensive measurement approaches, our framework supports simultaneous tracking of multiple worms while maintaining automated extraction of various behavioral parameters with high precision. Furthermore, our approach advances the standardization of C. elegans behavioral parameter analysis, which can analyze the behavioral data of multiple worms at the same time, significantly improving the experimental throughput and providing an efficient tool for drug screening, gene function research and other fields.Author summary: Studying the behavior of the worm C. elegans is crucial for understanding genetics and neurobiology, but manually tracking its movements is slow and impractical for large experiments. To overcome this, we developed an automated, high-speed system that uses advanced deep learning to accurately detect and track worms in real-time, even when they temporarily hide or touch each other. Our approach enhances the YOLOv8 architecture by incorporating a Convolutional Block Attention Module, which enables the model to focus on the most relevant visual features of the worms while suppressing background interference. Additionally, we modified the loss function to better handle the detection of small and overlapping worms, significantly improving localization accuracy. These technical innovations allow our method to follow multiple worms and automatically measure key behaviors like movement speed, body bending, and roll frequency with exceptional precision. By providing a robust and standardized algorithm, making advanced behavioral studies accessible to more biologists. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013707 DOI: 10.1371/journal.pcbi.1013707 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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