{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:56:42Z","timestamp":1743015402637,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031232350"},{"type":"electronic","value":"9783031232367"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23236-7_53","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T01:22:43Z","timestamp":1672536163000},"page":"776-787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Caenorhabditis Elegans Detection Using YOLOv5 and\u00a0Faster R-CNN Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-8970","authenticated-orcid":false,"given":"Ernesto Jes\u00fas","family":"Rico-Guardiola","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6753-7652","authenticated-orcid":false,"given":"Pablo E.","family":"Layana-Castro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3676-8287","authenticated-orcid":false,"given":"Antonio","family":"Garc\u00eda-Garv\u00ed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-5356","authenticated-orcid":false,"given":"Antonio-Jos\u00e9","family":"S\u00e1nchez-Salmer\u00f3n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"53_CR1","doi-asserted-by":"crossref","unstructured":"Bates, K., Le, K.N., Lu, H.: Deep learning for robust and flexible tracking in behavioral studies for C. elegans. PLOS Comput. Biol. 18(4), e1009942 (2022)","DOI":"10.1371\/journal.pcbi.1009942"},{"key":"53_CR2","doi-asserted-by":"publisher","unstructured":"Biron, D., Haspel, G. (eds.): C. elegans. MMB, vol. 1327. Humana Press, Totowa (2015). https:\/\/doi.org\/10.1007\/978-1-4939-2842-2","DOI":"10.1007\/978-1-4939-2842-2"},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Plant disease recognition model based on improved YOLOv5. Agronomy 12(2), 365 (2022)","DOI":"10.3390\/agronomy12020365"},{"key":"53_CR4","doi-asserted-by":"publisher","unstructured":"Di Rosa, G., et al.: Healthspan enhancement by olive polyphenols in C. elegans wild type and Parkinson\u2019s models. Int. J. Mol. Sci. 21(11) (2020). https:\/\/doi.org\/10.3390\/ijms21113893","DOI":"10.3390\/ijms21113893"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Fudickar, S., Nustede, E.J., Dreyer, E., Bornhorst, J.: Mask R-CNN based C. elegans detection with a DIY microscope. Biosensors 11(8), 257 (2021)","DOI":"10.3390\/bios11080257"},{"key":"53_CR6","doi-asserted-by":"publisher","unstructured":"Garc\u00eda Garv\u00ed, A., Puchalt, J.C., Layana Castro, P.E., Navarro Moya, F., S\u00e1nchez-Salmer\u00f3n, A.J.: Towards lifespan automation for Caenorhabditis elegans based on deep learning: analysing convolutional and recurrent neural networks for dead or live classification. Sensors 21(14) (2021). https:\/\/doi.org\/10.3390\/s21144943","DOI":"10.3390\/s21144943"},{"key":"53_CR7","doi-asserted-by":"publisher","unstructured":"Hahm, J.H., et al.: C. elegans maximum velocity correlates with healthspan and is maintained in worms with an insulin receptor mutation. Nat. Commun. 6(1), 1\u20137 (2015). https:\/\/doi.org\/10.1038\/ncomms9919","DOI":"10.1038\/ncomms9919"},{"key":"53_CR8","unstructured":"Iqbal, H.: Harisiqbal88\/plotneuralnet v1.0.0 (2018). code https:\/\/github.com\/HarisIqbal88\/PlotNeuralNet"},{"key":"53_CR9","doi-asserted-by":"publisher","unstructured":"Javer, A., et al.: An open-source platform for analyzing and sharing worm-behavior data. Nat. Methods 15 (2018). https:\/\/doi.org\/10.1038\/s41592-018-0112-1","DOI":"10.1038\/s41592-018-0112-1"},{"key":"53_CR10","doi-asserted-by":"publisher","unstructured":"Koopman, M., et al.: Assessing motor-related phenotypes of Caenorhabditis elegans with the wide field-of-view nematode tracking platform. Nat. Protoc. 15, 1\u201336 (2020). https:\/\/doi.org\/10.1038\/s41596-020-0321-9","DOI":"10.1038\/s41596-020-0321-9"},{"key":"53_CR11","doi-asserted-by":"publisher","unstructured":"Layana Castro, P.E., Puchalt, J.C., Garc\u00eda Garv\u00ed, A., S\u00e1nchez-Salmer\u00f3n, A.J.: Caenorhabditis elegans multi-tracker based on a modified skeleton algorithm. Sensors 21(16) (2021). https:\/\/doi.org\/10.3390\/s21165622","DOI":"10.3390\/s21165622"},{"key":"53_CR12","doi-asserted-by":"publisher","unstructured":"Layana Castro, P.E., Puchalt, J.C., S\u00e1nchez-Salmer\u00f3n, A.J.: Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Sci. Rep. 10(1), 22247 (2020). https:\/\/doi.org\/10.1038\/s41598-020-79430-8","DOI":"10.1038\/s41598-020-79430-8"},{"key":"53_CR13","doi-asserted-by":"publisher","unstructured":"Le, K.N., Zhan, M., Cho, Y., Wan, J., Patel, D.S., Lu, H.: An automated platform to monitor long-term behavior and healthspan in Caenorhabditis elegans under precise environmental control. Commun. Biol. 3(1), 1\u201313 (2020). https:\/\/doi.org\/10.1038\/s42003-020-1013-2","DOI":"10.1038\/s42003-020-1013-2"},{"key":"53_CR14","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"53_CR15","doi-asserted-by":"publisher","unstructured":"Olsen, A., Gill, M.S. (eds.): Ageing: Lessons from C. elegans. HAL. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-44703-2","DOI":"10.1007\/978-3-319-44703-2"},{"key":"53_CR16","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"53_CR17","doi-asserted-by":"publisher","unstructured":"Puchalt, J.C., Gonzalez-Rojo, J.F., G\u00f3mez-Escribano, A.P., V\u00e1zquez-Manrique, R.P., S\u00e1nchez-Salmer\u00f3n, A.J.: Multiview motion tracking based on a cartesian robot to monitor Caenorhabditis elegans in standard petri dishes. Sci. Rep. 12(1), 1\u201311 (2022). https:\/\/doi.org\/10.1038\/s41598-022-05823-6","DOI":"10.1038\/s41598-022-05823-6"},{"key":"53_CR18","doi-asserted-by":"publisher","unstructured":"Puchalt, J.C., Layana Castro, P.E., S\u00e1nchez-Salmer\u00f3n, A.J.: Reducing results variance in lifespan machines: an analysis of the influence of vibrotaxis on wild-type Caenorhabditis elegans for the death criterion. Sensors 20(21) (2020). https:\/\/doi.org\/10.3390\/s20215981","DOI":"10.3390\/s20215981"},{"key":"53_CR19","doi-asserted-by":"publisher","unstructured":"Puchalt, J.C., S\u00e1nchez-Salmer\u00f3n, A.J., Eugenio, I., Llopis, S., Mart\u00ednez, R., Martorell, P.: Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques. Sci. Rep. 11 (2021). https:\/\/doi.org\/10.1038\/s41598-021-91898-6","DOI":"10.1038\/s41598-021-91898-6"},{"key":"53_CR20","doi-asserted-by":"publisher","unstructured":"Puchalt, J.C., S\u00e1nchez-Salmer\u00f3n, A.J., Martorell Guerola, P., Genov\u00e9s Mart\u00ednez, S.: Active backlight for automating visual monitoring: an analysis of a lighting control technique for Caenorhabditis elegans cultured on standard petri plates. PLoS ONE 14(4), 1\u201318 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0215548","DOI":"10.1371\/journal.pone.0215548"},{"key":"53_CR21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"53_CR22","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28 (2015)"},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Stiernagle, T.: Maintenance of C. elegans. WormBook. The C. elegans research community. WormBook (2006)","DOI":"10.1895\/wormbook.1.101.1"}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23236-7_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:35:22Z","timestamp":1672540522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23236-7_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031232350","9783031232367"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23236-7_53","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bragan\u00e7a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ol2a.ipb.pt\/EN_index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}