{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T21:57:55Z","timestamp":1747173475330,"version":"3.40.5"},"reference-count":29,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Zygote"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Identifying embryos with a high potential for implementation remains a challenge in <jats:italic>in vitro<\/jats:italic> fertilization (IVF) cycles. Despite progress in IVF treatment, only a minority of generated embryos has the ability to implant. Another drawback of this practice is the high frequency of multiple pregnancies. This problem leads to economic and health problems. Therefore, the transfer of a single embryo with high implantation potential is the ideal strategy. Morphometric evaluation of two-pronucleus zygote images is a helpful technique when aiming to transfer a single embryo with a high implantation potential. In this study, an automated zygote morphometric evaluation algorithm, called the zygote morphology evaluation (ZME) algorithm, was created to analyze the zygote and provide morphological measurements. The first and most crucial step of the ZME algorithm is the noise reduction step, which was first applied to zygote images. After that, the proposed algorithm detects different parts of the zygote that are indicators of embryo viability and normality, that is the oolemma, perivitelline space, zona pellucida, and nucleolar precursor bodies (NPBs). In addition, a novel dataset was prepared for this task. This dataset consisted of 703 human zygote images, and called the human zygote morphometric evaluation dataset (HZME-DS). Our experimental results in the HZME-DS showed that the ZME algorithm was able to achieve 79.58% average accuracy in identifying the oolemma region, 79.40% average accuracy in determining the perivitelline space, and 79.72% accuracy in identifying the zona pellucida. To calculate the accuracy of identifying NPBs, the proposed algorithm uses Recall and Precision measures, and their harmonic average (F1 measure) reached values of 81.14% and 79.53%, respectively. These encouraging results for our proposed method, which is an automatic and very fast method, showed that the ZME algorithm could help embryologists to evaluate the best zygotes in real time and the best embryos subsequently.<\/jats:p>","DOI":"10.1017\/s0967199422000326","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T04:21:17Z","timestamp":1660710077000},"page":"819-829","source":"Crossref","is-referenced-by-count":1,"title":["Morphometric evaluation of two-pronucleus zygote images using image-processing techniques"],"prefix":"10.1017","volume":"30","author":[{"given":"Niloofar","family":"Sayadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Monji-Azad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8853-9112","authenticated-orcid":false,"given":"Seyed Abolghasem","family":"Mirroshandel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatemeh","family":"Ghasemian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"S0967199422000326_ref15","unstructured":"Morales, D. A. , Bengoetxea, E. and Larranaga, P. (2008). Automatic segmentation of zona pellucida in human embryo images applying an active contour model. Proceedings of the of MIUA."},{"key":"S0967199422000326_ref2","first-page":"5","article-title":"Methods of embryo scoring in in vitro fertilization","volume":"4","author":"B\u0105czkowski","year":"2004","journal-title":"Reproductive Biology"},{"key":"S0967199422000326_ref19","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.12818"},{"key":"S0967199422000326_ref25","first-page":"3859","volume-title":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Wang","year":"2015"},{"key":"S0967199422000326_ref12","doi-asserted-by":"publisher","DOI":"10.1007\/BF00133570"},{"volume-title":"Computer vision","year":"2001","author":"Shapiro","key":"S0967199422000326_ref22"},{"key":"S0967199422000326_ref21","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/15.11.2394"},{"key":"S0967199422000326_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2057552"},{"key":"S0967199422000326_ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10815-021-02123-2"},{"key":"S0967199422000326_ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"volume-title":"Automatic detection and identification of cells in digital images of day 2 IVF embryos","year":"2013","author":"El-Shenawy","key":"S0967199422000326_ref7"},{"key":"S0967199422000326_ref28","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/12.7.1545"},{"key":"S0967199422000326_ref151","doi-asserted-by":"crossref","unstructured":"M\u00f6lder, A. , Czanner, S. , Costen, N. and Hartshorne, G. (2014). Automatic detection of embryo location in medical imaging using trigonometric rotation for noise reduction. In: 2014 22nd International Conference on Pattern Recognition (pp. 3239\u20133244). IEEE.","DOI":"10.1109\/ICPR.2014.558"},{"key":"S0967199422000326_ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICETET.2009.220"},{"key":"S0967199422000326_ref18","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.humrep.a136618"},{"key":"S0967199422000326_ref24","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/deaa013"},{"key":"S0967199422000326_ref9","unstructured":"Giusti, A. , Corani, G. , Gambardella, L. , Magli, C. and Gianaroli, L. (2009). Segmentation of human zygotes in Hoffman modulation contrast images. Proceedings of the of MIUA."},{"volume-title":"Pattern recognition with fuzzy objective function algorithms","year":"2013","author":"Bezdek","key":"S0967199422000326_ref5"},{"key":"S0967199422000326_ref16","unstructured":"Powers, D. M. (2011). 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