Mask R-CNN Based C. Elegans Detection with a DIY Microscope
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/11/8/257 |
| _version_ | 1797524506653229056 |
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| author | Sebastian Fudickar Eike Jannik Nustede Eike Dreyer Julia Bornhorst |
| author_facet | Sebastian Fudickar Eike Jannik Nustede Eike Dreyer Julia Bornhorst |
| author_sort | Sebastian Fudickar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:58:23Z |
| format | Article |
| id | doaj.art-5bbfb4200bae4673abe4888ac8321682 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T08:58:23Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-5bbfb4200bae4673abe4888ac83216822023-11-22T06:57:27ZengMDPI AGBiosensors2079-63742021-07-0111825710.3390/bios11080257Mask R-CNN Based C. Elegans Detection with a DIY MicroscopeSebastian Fudickar0Eike Jannik Nustede1Eike Dreyer2Julia Bornhorst3Assistance Systems and Medical Device Technology, Faculty of Medicine and Health Sciences, CvO University of Oldenburg, Ammerländer Heerstraße 140, 26129 Oldenburg, GermanyAssistance Systems and Medical Device Technology, Faculty of Medicine and Health Sciences, CvO University of Oldenburg, Ammerländer Heerstraße 140, 26129 Oldenburg, GermanyAssistance Systems and Medical Device Technology, Faculty of Medicine and Health Sciences, CvO University of Oldenburg, Ammerländer Heerstraße 140, 26129 Oldenburg, GermanyFaculty Mathematik und Naturwissenschaften, Bergische Universität Wuppertal, Lebensmittelchemie, Gaußstr. 20, 42119 Wuppertal, GermanyCaenorhabditis 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.https://www.mdpi.com/2079-6374/11/8/257C. eleganssegmentationclassificationDIY microscopemask R-CNN |
| spellingShingle | Sebastian Fudickar Eike Jannik Nustede Eike Dreyer Julia Bornhorst Mask R-CNN Based C. Elegans Detection with a DIY Microscope Biosensors C. elegans segmentation classification DIY microscope mask R-CNN |
| title | Mask R-CNN Based C. Elegans Detection with a DIY Microscope |
| title_full | Mask R-CNN Based C. Elegans Detection with a DIY Microscope |
| title_fullStr | Mask R-CNN Based C. Elegans Detection with a DIY Microscope |
| title_full_unstemmed | Mask R-CNN Based C. Elegans Detection with a DIY Microscope |
| title_short | Mask R-CNN Based C. Elegans Detection with a DIY Microscope |
| title_sort | mask r cnn based c elegans detection with a diy microscope |
| topic | C. elegans segmentation classification DIY microscope mask R-CNN |
| url | https://www.mdpi.com/2079-6374/11/8/257 |
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