ArticleScientific reports2025
A high precision method of segmenting complex postures in Caenorhabditis elegans and deep phenotyping to analyze lifespan.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.PLoS computational biology · 2026Article
- WormSpot: a machine learning-powered viability scoring platform inMicrobiology spectrum · 2026Article
- From manual counting to YOLO: Using computer vision to automate large-scale fecundity assays in C. elegans.PloS one · 2026Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
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Authors and funding
2 authors.
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Abstract
In-depth exploration of the effects of genes on the development, physiology, and behavior of organisms requires high-precision phenotypic analysis. However, the overlap of body postures in group behavior and the similarity of movement patterns between strains pose challenges to accuracy analysis. To address this issue, we designed the WormYOLO model based on the YOLO architecture, which improves the segmentation performance of C .elegans and effectively handles overlapping poses in images. In detection and segmentation tasks, WormYOLO performs well on the more overlapping Mating dataset, with its object detection performance improving by 24.1% (mAP
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