ArticleScientific reports2023
WormSwin: Instance segmentation of C. elegans using vision transformer.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- Deep-Pose-Tracker: an automated behavioural analysis framework for Caenorhabditis elegans.Scientific reports · 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
- Guide frames improve centerline estimation of complex postures in WormTracer.microPublication biology · 2026Article
- Identification of plant-parasitic nematode genera in turfgrass using deep learning algorithms.Scientific reports · 2025Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
- A neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens.PLoS computational biology · 2025Article
- Article
- A high precision method of segmenting complex postures in Caenorhabditis elegans and deep phenotyping to analyze lifespan.Scientific reports · 2025Article
- Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.Scientific reports · 2025Article
- Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing.Scientific reports · 2025Article
- vivoBodySeg: Machine learning-based analysis of C. elegans immobilized in vivoChip for automated developmental toxicity testing.Research square · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The possibility to extract motion of a single organism from video recordings at a large-scale provides means for the quantitative study of its behavior, both individual and collective. This task is particularly difficult for organisms that interact with one another, overlap, and occlude parts of their bodies in the recording. Here we propose WormSwin-an approach to extract single animal postures of Caenorhabditis elegans (C. elegans) from recordings of many organisms in a single microscope well. Based on transformer neural network architecture our method segments individual worms across a range of videos and images generated in different labs. Our solutions offers accuracy of 0.990 average precision ([Formula: see text]) and comparable results on the benchmark image dataset BBBC010. Finally, it allows to segment challenging overlapping postures of mating worms with an accuracy sufficient to track the organisms with a simple tracking heuristic. An accurate and efficient method for C. elegans segmentation opens up new opportunities for studying of its behaviors previously inaccessible due to the difficulty in the worm extraction from the video frames.
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