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However, the process of evaluating numerous images captured by time\u2010lapse imaging (TLI) system is time\u2010consuming and some important features cannot be recognized by naked eyes. Convolutional neural network (CNN) is used in medical imaging yet in IVF. The study aims to apply CNN on day\u2010one human embryo TLI. We first presented CNN algorithm for day\u2010one human embryo segmentation on three distinct features: zona pellucida (ZP), cytoplasm and pronucleus (PN). We tested the CNN performance compared side\u2010by\u2010side with manual labelling by clinical embryologist, then measured the segmented day\u2010one human embryo parameters and compared them with literature reported values. The precisions of segmentation were that cytoplasm over 97%, PN over 84% and ZP around 80%. For the morphometrics data of cytoplasm, ZP and PN, the results were comparable with those reported in literatures, which showed high reproducibility and consistency. The CNN system provides fast and stable analytical outcome to improve work efficiency in IVF setting. To conclude, our CNN system is potential to be applied in practice for day\u2010one human embryo segmentation as a robust tool with high precision, reproducibility and speed.<\/jats:p>","DOI":"10.1111\/jcmm.16288","type":"journal-article","created":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T09:55:02Z","timestamp":1611482102000},"page":"2633-2644","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Application of convolutional neural network on early human embryo segmentation during in vitro fertilization"],"prefix":"10.1111","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3965-7048","authenticated-orcid":false,"given":"Mingpeng","family":"Zhao","sequence":"first","affiliation":[{"name":"Assisted Reproductive Technology Unit Department of Obstetrics and Gynaecology Faculty of Medicine The Chinese University of Hong Kong Hong Kong 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