Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection

Sensors

Sebastian Fudickar¹  EikeJannik Nustede¹  Julia Bornhorst² 

[1] Assistance Systems and Medical Device Technology, Department of Health Services Research, Faculty of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Ammerlaender Heerstr. 114-118, 26129 Oldenburg, Germany;Department of Food Chemistry, Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany;

关键词: Caenorhabditis elegans;    machine learning;    smartphone;    microscope;    SVM;    HOG;   

DOI  :  10.3390/s19061468

来源: DOAJ

【 摘 要 】

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 general practicability of the chosen approach.

【 授权许可】

Unknown