Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection
- The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the generalThe nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.…
| Author details: | Julia BornhorstORCiDGND, Eike Jannik Nustede, Sebastian FudickarORCiD |
|---|---|
| DOI: | https://doi.org/10.3390/s19061468 |
| ISSN: | 1424-8220 |
| Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/30917520 |
| Title of parent work (English): | Sensors |
| Publisher: | MDPI |
| Place of publishing: | Basel |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2019/03/26 |
| Publication year: | 2019 |
| Release date: | 2021/03/15 |
| Tag: | Caenorhabditis elegans; HOG; SVM; machine learning; microscope; smartphone |
| Volume: | 19 |
| Issue: | 6 |
| Number of pages: | 14 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Ernährungswissenschaft |
| DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
| Peer review: | Referiert |
| Publishing method: | Open Access / Gold Open-Access |
| DOAJ gelistet | |
| License (German): |  CC-BY - Namensnennung 4.0 International |
