{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T18:40:32Z","timestamp":1784400032758,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Effat College of Engineering, Effat University, Jeddah 22332, Saudi Arabia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in a specialized environment and kept for growth. Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. The morphology of the embryological components is highly related to the success of the assisted reproduction procedure. In approximately 3\u20135 days, the embryo transforms into the blastocyst. To prevent the multiple-birth risk and to increase the chance of pregnancy the embryologist manually analyzes the blastocyst components and selects valuable embryos to transfer to the women\u2019s uterus. The manual microscopic analysis of blastocyst components, such as trophectoderm, zona pellucida, blastocoel, and inner cell mass, is time-consuming and requires keen expertise to select a viable embryo. Artificial intelligence is easing medical procedures by the successful implementation of deep learning algorithms that mimic the medical doctor\u2019s knowledge to provide a better diagnostic procedure that helps in reducing the diagnostic burden. The deep learning-based automatic detection of these blastocyst components can help to analyze the morphological properties to select viable embryos. This research presents a deep learning-based embryo component segmentation network (ECS-Net) that accurately detects trophectoderm, zona pellucida, blastocoel, and inner cell mass for embryological analysis. The proposed method (ECS-Net) is based on a shallow deep segmentation network that uses two separate streams produced by a base convolutional block and a depth-wise separable convolutional block. Both streams are densely concatenated in combination with two dense skip paths to produce powerful features before and after upsampling. The proposed ECS-Net is evaluated on a publicly available microscopic blastocyst image dataset, the experimental segmentation results confirm the efficacy of the proposed method. The proposed ECS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% for embryological analysis.<\/jats:p>","DOI":"10.3390\/s22197418","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:09:29Z","timestamp":1664492969000},"page":"7418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization"],"prefix":"10.3390","volume":"22","author":[{"given":"Abeer","family":"Mushtaq","sequence":"first","affiliation":[{"name":"Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Mumtaz","sequence":"additional","affiliation":[{"name":"Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Primary and Secondary Healthcare, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8440-4111","authenticated-orcid":false,"given":"Nema","family":"Salem","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Effat College of Engineering, Effat University, Jeddah 22332, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-1676","authenticated-orcid":false,"given":"Muhammad Naveed","family":"Yasir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Narowal, Narowal 51600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.cegh.2020.08.008","article-title":"Prevalence and potential determinants of primary infertility in India: Evidence from Indian demographic health survey","volume":"9","author":"Purkayastha","year":"2021","journal-title":"Clin. Epidemiol. Glob. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1159\/000343058","article-title":"Historical walk: The HRP special programme and infertility","volume":"74","year":"2012","journal-title":"Gynecol. Obstet. Investig."},{"key":"ref_3","first-page":"15","article-title":"Infertility in India: Levels, patterns and consequences-Priorities for social science research","volume":"44","author":"Jejeebhoy","year":"1998","journal-title":"J. Fam. Welf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1016\/j.cjca.2021.09.002","article-title":"Infertility, Infertility Treatment, and Cardiovascular Disease: An Overview","volume":"37","author":"Smith","year":"2021","journal-title":"Can. J. Cardiol."},{"key":"ref_5","first-page":"671","article-title":"Depression, anxiety, quality of life and coping in women with infertility: A cross-sectional study from India","volume":"3","author":"Dadhwal","year":"2021","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_6","unstructured":"Choe, J., Archer, J.S., and Shanks, A.L. (2020). Vitro Fertilization, StatPearls Publishing."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1111\/jcmm.16288","article-title":"Application of convolutional neural network on early human embryo segmentation during in vitro fertilization","volume":"25","author":"Zhao","year":"2021","journal-title":"J. Cell. Mol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.bpobgyn.2018.07.001","article-title":"Single embryo transfer for all","volume":"53","author":"Cutting","year":"2018","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s12958-016-0181-x","article-title":"Different effectiveness of closed embryo culture system with time-lapse imaging (EmbryoScopeTM) in comparison to standard manual embryology in good and poor prognosis patients: A prospectively randomized pilot study","volume":"14","author":"Wu","year":"2016","journal-title":"Reprod. Biol. Endocrinol."},{"key":"ref_10","first-page":"1","article-title":"Human blastocyst formation and development","volume":"5","author":"Lubis","year":"2018","journal-title":"Embryol.-Theory Pract."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12958-020-00595-y","article-title":"Association between the quality of inner cell mass and first trimester miscarriage after single blastocyst transfer","volume":"18","author":"Shi","year":"2020","journal-title":"Reprod. Biol. Endocrinol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/S0015-0282(01)02870-9","article-title":"Quantitative grading of a human blastocyst: Optimal inner cell mass size and shape","volume":"76","author":"Richter","year":"2001","journal-title":"Fertil. Steril."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/s41598-018-36452-7","article-title":"Identification of extracellular vesicles and characterization of miRNA expression profiles in human blastocoel fluid","volume":"9","author":"Battaglia","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.4103\/0366-6999.232808","article-title":"Overall blastocyst quality, trophectoderm grade, and inner cell mass grade predict pregnancy outcome in euploid blastocyst transfer cycles","volume":"131","author":"Zhao","year":"2018","journal-title":"Chin. Med. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TBME.2015.2466098","article-title":"Automatic dissection position selection for cleavage-stage embryo biopsy","volume":"63","author":"Wang","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101985","DOI":"10.1016\/j.media.2021.101985","article-title":"A survey on incorporating domain knowledge into deep learning for medical image analysis","volume":"69","author":"Xie","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Razzak, I., Shoukat, G., Naz, S., and Khan, T.M. (2020, January 19\u201324). Skin Lesion Analysis Toward Accurate Detection of Melanoma using Multistage Fully Connected Residual Network. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206881"},{"key":"ref_19","first-page":"1","article-title":"A survey on deep learning-based architectures for semantic segmentation on 2d images","volume":"1","author":"Ulku","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_20","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"7","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.rbmo.2020.12.008","article-title":"Cytoplasmic movements of the early human embryo: Imaging and artificial intelligence to predict blastocyst development","volume":"42","author":"Coticchio","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.rbmo.2021.05.002","article-title":"Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth","volume":"43","author":"Sawada","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TBME.2014.2356415","article-title":"Automatic segmentation of trophectoderm in microscopic images of human blastocysts","volume":"62","author":"Singh","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., Singh, A., Saeedi, P., Au, J., and Havelock, J. (2017, January 17\u201320). Inner cell mass segmentation in human HMC embryo images using fully convolutional network. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296582"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rad, R.M., Saeedi, P., Au, J., and Havelock, J. (2018, January 7\u201310). Multi-resolutional ensemble of stacked dilated u-net for inner cell mass segmentation in human embryonic images. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451750"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.imu.2018.10.009","article-title":"Human Blastocyst\u2019s Zona Pellucida segmentation via boosting ensemble of complementary learning","volume":"13","author":"Rad","year":"2018","journal-title":"Inf. Med. Unlocked"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101612","DOI":"10.1016\/j.media.2019.101612","article-title":"Trophectoderm segmentation in human embryo images via inceptioned U-Net","volume":"62","author":"Rad","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., Saeedi, P., and Bajic, I. (2016, January 15\u201318). Human blastocyst segmentation using neural network. Proceedings of the 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, Canada.","DOI":"10.1109\/CCECE.2016.7726763"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1109\/TBME.2017.2759665","article-title":"Automatic identification of human blastocyst components via texture","volume":"64","author":"Saeedi","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rad, R.M., Saeedi, P., Au, J., and Havelock, J. (2019, January 22\u201325). BLAST-NET: Semantic segmentation of human blastocyst components via cascaded atrous pyramid and dense progressive upsampling. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803139"},{"key":"ref_32","first-page":"435","article-title":"Artificial intelligence in the embryology laboratory: A review","volume":"3","author":"Dimitriadis","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_38","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern ecognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_40","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7418\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:41:59Z","timestamp":1760143319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,29]]},"references-count":40,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197418"],"URL":"https:\/\/doi.org\/10.3390\/s22197418","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,29]]}}}