ArticleScientific reports2020
Improving skeleton algorithm for helping Caenorhabditis elegans trackers.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN-transformer models.Scientific reports · 2026Article
- Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.Scientific reports · 2025Article
- Network Flow Method Integrates Skeleton Information for MultipleSensors (Basel, Switzerland) · 2025Article
- Automated scoring of nematode nictation on a textured background.bioRxiv : the preprint server for biology · 2023Article
- Automatic segmentation ofHeliyon · 2023Article
- Continuous identification of the tea shoot tip and accurate positioning of picking points for a harvesting from standard plantations.Frontiers in plant science · 2023Article
- Article
- Towards generalization forComputational and structural biotechnology journal · 2023Article
- Article
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Authors and funding
3 authors.
Funding
Abstract
One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.
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Registered trials
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