{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:07:44Z","timestamp":1768424864506,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031170294","type":"print"},{"value":"9783031170300","type":"electronic"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":397,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Assisted reproductive technology (ART) refers to treatments of infertility which include the handling of eggs, sperm and embryos. The success of ART procedures depends on several factors, including the quality of the embryo transferred to the woman. The assessment of embryos is mostly based on the morphokinetic parameters of their development, which include the number of cells at a given time point indicating the cell stage and the duration of each cell stage. In many clinics, time-lapse imaging systems are used for continuous visual inspection of the embryo development. However, the analysis of time-lapse data still requires the evaluation, by embryologists, of the morphokinetic parameters and cleavage patterns, making the assessment subjective. Recently the application of object detection in the field of medical imaging enabled the accurate detection of lesion or object of interest. Motivated by this research direction, we proposed a methodology to detect and track cells present inside embryos in time-lapse image series. The methodology employed an object detection technique called YOLO v5 and annotated the start of observed cell stages based on the cell count. Our approach could identify cell division to detect cell cleavage or start of next cell stage accurately up to the 5-cell stage. The methodology also highlighted instances of embryos development with abnormal cell cleavage patterns. On an average the methodology used 8\u00a0s to annotate a video frame (20 frames per second), which will not pose any delay for the embryologists while assessing embryo quality. The results were validated by embryologists, and they considered the methodology as a useful tool for their clinical practice.<\/jats:p>","DOI":"10.1007\/978-3-031-17030-0_7","type":"book-chapter","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T13:40:37Z","timestamp":1675258837000},"page":"81-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detecting Human Embryo Cleavage Stages Using YOLO V5 Object Detection Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-7938","authenticated-orcid":false,"given":"Akriti","family":"Sharma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5870-0999","authenticated-orcid":false,"given":"Mette H.","family":"Stensen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7958-2899","authenticated-orcid":false,"given":"Erwan","family":"Delbarre","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1874-7789","authenticated-orcid":false,"given":"Momin","family":"Siddiqui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8814-3393","authenticated-orcid":false,"given":"Trine B.","family":"Haugen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-2064","authenticated-orcid":false,"given":"Michael A.","family":"Riegler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-7148","authenticated-orcid":false,"given":"Hugo L.","family":"Hammer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"7_CR1","unstructured":"Angeles, P.F.C.L.: Day 3 vs. day 5 embryo transfers (2019). https:\/\/www.pfcla.com\/blog\/day-3-vs-day-5-embryo-transfer"},{"key":"7_CR2","unstructured":"Bandyopadhyay, H.: Yolo: Real-time object detection explained (2022). https:\/\/www.v7labs.com\/blog\/yolo-object-detection"},{"key":"7_CR3","doi-asserted-by":"publisher","unstructured":"Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020). https:\/\/doi.org\/10.1186\/s12864-019-6413-7","DOI":"10.1186\/s12864-019-6413-7"},{"key":"7_CR4","doi-asserted-by":"publisher","unstructured":"Cimadomo, D., et al.: P-210 abnormal cleavage patterns during embryo preimplantation development and their effect on blastulation: an overview from IVF patients with multiple IVF cycles in a time-lapse incubator. Human Reprod. 36(Supplement_1), 230\u2013231 (2021). https:\/\/doi.org\/10.1093\/humrep\/deab130.209","DOI":"10.1093\/humrep\/deab130.209"},{"key":"7_CR5","doi-asserted-by":"publisher","unstructured":"Cummins, J.M., Breen, T.M., Harrison, K.L., Shaw, J.M., Wilson, L.M., Hennessey, J.F.: A formula for scoring human embryo growth rates in in vitro fertilization: its value in predicting pregnancy and in comparison with visual estimates of embryo quality. J. In Vitro Fert Embryo Transf. 3(5), 284\u2013295 (1986). https:\/\/doi.org\/10.1186\/s12938-019-0738-y","DOI":"10.1186\/s12938-019-0738-y"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Desai, N., Goldberg, J.M., Austin, C., Falcone, T.: Are cleavage anomalies, multinucleation, or specific cell cycle kinetics observed with time-lapse imaging predictive of embryo developmental capacity or ploidy? Fertility Sterility 109(4), 665\u2013674 (2018). https:\/\/doi.org\/10.1016\/j.fertnstert.2017.12.025, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0015028217321726","DOI":"10.1016\/j.fertnstert.2017.12.025"},{"key":"7_CR7","doi-asserted-by":"publisher","unstructured":"Gallego, R.D., Remoh\u00ed, J., Meseguer, M.: Time-lapse imaging: the state of the art$$^\\dagger $$. Biol. Reprod. 101(6), 1146\u20131154 (2019). https:\/\/doi.org\/10.1093\/biolre\/ioz035","DOI":"10.1093\/biolre\/ioz035"},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Kaser, D.J., Racowsky, C.: Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: a systematic review. Human Reprod. Update 20(5), 617\u2013631 (2014). https:\/\/doi.org\/10.1093\/humupd\/dmu023","DOI":"10.1093\/humupd\/dmu023"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Kaur, A., Singh, Y., Neeru, N., Kaur, L., Singh, A.: A survey on deep learning approaches to medical images and a systematic look up into real-time object detection. Arch. Comput. Methods Eng. 29, 2071\u20132111 (2021). https:\/\/doi.org\/10.1007\/s11831-021-09649-9","DOI":"10.1007\/s11831-021-09649-9"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Li, F., Abozaid, T., et al.: Multinucleation on 2-cell stage and reverse cleavage may not impact implantation outcomes: a time-lapse study. Fertility Sterility 102, E135 (2014). https:\/\/doi.org\/10.1016\/j.fertnstert.2014.07.461","DOI":"10.1016\/j.fertnstert.2014.07.461"},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neucom.2019.04.028","volume":"350","author":"Z Li","year":"2019","unstructured":"Li, Z., Dong, M., Wen, S., Hu, X., Zhou, P., Zeng, Z.: CLU-CNNs: object detection for medical images. Neurocomputing 350, 53\u201359 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.04.028","journal-title":"Neurocomputing"},{"key":"7_CR13","doi-asserted-by":"publisher","unstructured":"Liu, Y., Chapple, V., Roberts, P., Matson, P.: Prevalence, consequence, and significance of reverse cleavage by human embryos viewed with the use of the embryoscope time-lapse video system. Fertility Sterility 102, 1295\u20131300 (2014). https:\/\/doi.org\/10.1016\/j.fertnstert.2014.07.1235","DOI":"10.1016\/j.fertnstert.2014.07.1235"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Paulson, R.J.: Time-lapse imaging. Fertility Sterility 109(4), 583 (2018). https:\/\/doi.org\/10.1016\/j.fertnstert.2018.02.013, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S001502821830089X","DOI":"10.1016\/j.fertnstert.2018.02.013"},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Rubio, I., et al.: Limited implantation success of direct-cleaved human zygotes: a time-lapse study. Fertility Sterility 98, 1458\u20131463 (2012). https:\/\/doi.org\/10.1016\/j.fertnstert.2012.07.1135","DOI":"10.1016\/j.fertnstert.2012.07.1135"},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Storr, A., Venetis, C.A., Cooke, S., Kilani, S., Ledger, W.: Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study. Human Reprod. 32(2), 307\u2013314 (2017). https:\/\/doi.org\/10.1093\/humrep\/dew330","DOI":"10.1093\/humrep\/dew330"},{"key":"7_CR18","doi-asserted-by":"publisher","unstructured":"Wong, C., et al.: Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat. Biotechnol. 28, 1115\u20131121 (2010). https:\/\/doi.org\/10.1038\/nbt.1686","DOI":"10.1038\/nbt.1686"},{"key":"7_CR19","doi-asserted-by":"publisher","unstructured":"Zaninovic, N., Ye, Z., Zhan, Q., Clarke, R., Rosenwaks, Z.: Cell stage onsets, embryo developmental potential and chromosomal abnormalities in embryos exhibiting direct unequal cleavages (DUCs). Fertility Sterility 100, S242 (2013). https:\/\/doi.org\/10.1016\/j.fertnstert.2013.07.1223","DOI":"10.1016\/j.fertnstert.2013.07.1223"}],"container-title":["Communications in Computer and Information Science","Nordic Artificial Intelligence Research and Development"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17030-0_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T14:13:26Z","timestamp":1675260806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17030-0_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031170294","9783031170300"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17030-0_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Symposium of the Norwegian AI Society","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oslo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","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":"31 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nais12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aisociety.no\/nais2022\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","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":"11","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":"0","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":"65% - 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":"0.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}