{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:27:23Z","timestamp":1775737643981,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["288727"],"award-info":[{"award-number":["288727"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Assisted reproductive technology is used for treating infertility, and its success relies on the quality and viability of embryos chosen for uterine transfer. Currently, embryologists manually assess embryo development, including the time duration between the cell cleavages. This paper introduces a machine learning methodology for automating the computations for the start of cell cleavage stages, in hours post insemination, in time-lapse videos. The methodology detects embryo cells in video frames and predicts the frame with the onset of the cell cleavage stage. Next, the methodology reads hours post insemination from the frame using optical character recognition. Unlike traditional embryo cell detection techniques, our suggested approach eliminates the need for extra image processing tasks such as locating embryos or removing extracellular material (fragmentation). The methodology accurately predicts cell cleavage stages up to five cells. The methodology was also able to detect the morphological structures of later cell cleavage stages, such as morula and blastocyst. It takes about one minute for the methodology to annotate the times of all the cell cleavages in a time-lapse video.<\/jats:p>","DOI":"10.3390\/bdcc7020091","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T03:32:27Z","timestamp":1683689547000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Cell Cleavage Timings from Time-Lapse Videos of Human Embryos"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-7938","authenticated-orcid":false,"given":"Akriti","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Computer Science, Oslo Metropolitan University, 0130 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5672-514X","authenticated-orcid":false,"given":"Ayaz Z.","family":"Ansari","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi 110025, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4185-6182","authenticated-orcid":false,"given":"Radhika","family":"Kakulavarapu","sequence":"additional","affiliation":[{"name":"Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5870-0999","authenticated-orcid":false,"given":"Mette H.","family":"Stensen","sequence":"additional","affiliation":[{"name":"Fertilitetssenteret, Pilestredet Park, 0176 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-2064","authenticated-orcid":false,"given":"Michael A.","family":"Riegler","sequence":"additional","affiliation":[{"name":"Department of Holistic Systems, SimulaMet, 0167 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-7148","authenticated-orcid":false,"given":"Hugo L.","family":"Hammer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Oslo Metropolitan University, 0130 Oslo, Norway"},{"name":"Department of Holistic Systems, SimulaMet, 0167 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2000080","DOI":"10.1002\/aisy.202000080","article-title":"Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep-Learning","volume":"2","author":"Zabari","year":"2020","journal-title":"Adv. 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