ArticleCommunications biology2023
Fast detection of slender bodies in high density microscopy data.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 21 citations in OpenAlex.
- Deep-Pose-Tracker: an automated behavioural analysis framework for Caenorhabditis elegans.Scientific reports · 2026Article
- Automated analysis of C. elegans behavior by LabGym: an open-source, AI-powered platform.G3 (Bethesda, Md.) · 2026Article
- Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.PLoS computational biology · 2026Article
- High-Throughput Toxicity Screening withEnvironmental science & technology · 2026Review
- Guide frames improve centerline estimation of complex postures in WormTracer.microPublication biology · 2026Article
- Automated analysis ofbioRxiv : the preprint server for biology · 2025Article
- A neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens.PLoS computational biology · 2025Article
- High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing.Scientific reports · 2025Article
- Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.Scientific reports · 2025Article
- Automated scoring of nematode nictation on a textured background.bioRxiv : the preprint server for biology · 2023Article
- WormSwin: Instance segmentation of C. elegans using vision transformer.Scientific reports · 2023Article
- Towards generalization forComputational and structural biotechnology journal · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.