MIDL 2021

 
International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

Mask R-CNN Based C. Elegans Detection with a DIY Microscope

TitleMask R-CNN Based C. Elegans Detection with a DIY Microscope
Publication TypeJournal Article
Year of Publication2021
AuthorsFudickar S., Nustede E.J., Dreyer E., Bornhorst J.
JournalBiosensors
Volume11
Issue8
Date Published2021
Publication Languageeng
ISSN2079-6374
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.

URLhttps://www.mdpi.com/2079-6374/11/8/257
DOI10.3390/bios11080257
Created at November 22, 2021 - 2:56pm by Fudickar.

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