ArticleBiosensors2021
Mask R-CNN Based C. Elegans Detection with a DIY Microscope.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 39 citations in OpenAlex.
- Motilitometer: A compact modular microscope for motility and photoresponse analysis of microorganisms.HardwareX · 2026Article
- From manual counting to YOLO: Using computer vision to automate large-scale fecundity assays in C. elegans.PloS one · 2026Article
- SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.Computers in biology and medicine · 2025Article
- Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?BMC medical informatics and decision making · 2025Article
- Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.Plants (Basel, Switzerland) · 2024Review
- Automating parasite egg detection: insights from the first AI-KFM challenge.Frontiers in artificial intelligence · 2024Article
- A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- WormSwin: Instance segmentation of C. elegans using vision transformer.Scientific reports · 2023Article
- Deep Learning for Microfluidic-AssistedMicromachines · 2023Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- A Compact Imaging Platform for ConductingLife (Basel, Switzerland) · 2023Article
- Towards generalization forComputational and structural biotechnology journal · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Automation ofComputational and structural biotechnology journal · 2023Article
- A Versatile Semester-Long Course-Based Undergraduate Research Experience using Optogenetics and RNAi to Identify Genes Important for Synapse Function.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2023Article
- Article
- Estimation of Caenorhabditis Elegans Lifespan Stages Using a Dual-Path Network Combining Biomarkers and Physiological Changes.Bioengineering (Basel, Switzerland) · 2022Article
- Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation.Micromachines · 2022Article
- Image recognition based on deep learning inComputational and structural biotechnology journal · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an AP@0.5 IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.
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Registered trials
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.