Towards generalization for Caenorhabditis elegans detection
The nematode Caenorhabditis elegans (C. elegans) is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C. elegans...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023003525 |
| _version_ | 1797384071100235776 |
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| author | Santiago Escobar-Benavides Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón |
| author_facet | Santiago Escobar-Benavides Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón |
| author_sort | Santiago Escobar-Benavides |
| collection | DOAJ |
| description | The nematode Caenorhabditis elegans (C. elegans) is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C. elegans detection algorithm, as previous work only focused on dataset-specific detection, tailored exclusively to the characteristics and appearance of the images in a given dataset. The main aim of our study is to achieve a solution that allows for robust detection, regardless of the image-capture system used, with the potential to serve as a basis for the automation of numerous assays. These potential applications include worm counting, worm tracking, motion detection and motion characterization. To train this model, a dataset consisting of a wide variety of appearances adopted by C. elegans has been curated and dataset augmentation methods have been proposed and evaluated, including synthetic image generation. The results show that the model achieves an average precision of 89.5% for a wide variety of C. elegans appearances that were not used during training, thereby validating its generalization capabilities. |
| first_indexed | 2024-03-08T21:30:11Z |
| format | Article |
| id | doaj.art-7a26361c6e534af5a1c3b266ca82d99a |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:30:11Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-7a26361c6e534af5a1c3b266ca82d99a2023-12-21T07:32:14ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012149144922Towards generalization for Caenorhabditis elegans detectionSantiago Escobar-Benavides0Antonio García-Garví1Pablo E. Layana-Castro2Antonio-José Sánchez-Salmerón3Instituto de Automática e Informática Industrial, Camino de Vera S/N, Valencia, 46022, SpainInstituto de Automática e Informática Industrial, Camino de Vera S/N, Valencia, 46022, SpainInstituto de Automática e Informática Industrial, Camino de Vera S/N, Valencia, 46022, SpainCorresponding author.; Instituto de Automática e Informática Industrial, Camino de Vera S/N, Valencia, 46022, SpainThe nematode Caenorhabditis elegans (C. elegans) is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C. elegans detection algorithm, as previous work only focused on dataset-specific detection, tailored exclusively to the characteristics and appearance of the images in a given dataset. The main aim of our study is to achieve a solution that allows for robust detection, regardless of the image-capture system used, with the potential to serve as a basis for the automation of numerous assays. These potential applications include worm counting, worm tracking, motion detection and motion characterization. To train this model, a dataset consisting of a wide variety of appearances adopted by C. elegans has been curated and dataset augmentation methods have been proposed and evaluated, including synthetic image generation. The results show that the model achieves an average precision of 89.5% for a wide variety of C. elegans appearances that were not used during training, thereby validating its generalization capabilities.http://www.sciencedirect.com/science/article/pii/S2001037023003525C. elegansDetection networkYOLO |
| spellingShingle | Santiago Escobar-Benavides Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón Towards generalization for Caenorhabditis elegans detection Computational and Structural Biotechnology Journal C. elegans Detection network YOLO |
| title | Towards generalization for Caenorhabditis elegans detection |
| title_full | Towards generalization for Caenorhabditis elegans detection |
| title_fullStr | Towards generalization for Caenorhabditis elegans detection |
| title_full_unstemmed | Towards generalization for Caenorhabditis elegans detection |
| title_short | Towards generalization for Caenorhabditis elegans detection |
| title_sort | towards generalization for caenorhabditis elegans detection |
| topic | C. elegans Detection network YOLO |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023003525 |
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