{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:49:05Z","timestamp":1743040145462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031627989"},{"type":"electronic","value":"9783031627996"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62799-6_22","type":"book-chapter","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T11:27:56Z","timestamp":1718105276000},"page":"213-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image Processing and\u00a0Deep Learning Methods for\u00a0the\u00a0Semantic Segmentation of\u00a0Blastocyst Structures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1457-4270","authenticated-orcid":false,"given":"Mar\u00eda","family":"Villota","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2564-6038","authenticated-orcid":false,"given":"Jacobo","family":"Ayensa-Jim\u00e9nez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-6452","authenticated-orcid":false,"given":"Manuel","family":"Doblar\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-1306","authenticated-orcid":false,"given":"J\u00f3nathan","family":"Heras","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Arsalan, M., et al.: Human blastocyst components detection using multiscale aggregation semantic segmentation network for embryonic analysis. Biomedicines 10(7) (2022)","DOI":"10.3390\/biomedicines10071717"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L.C., et al.: DeepLab: Semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Farias, A.F.S., et al.: Automated identification of blastocyst regions at different development stages. Sci. Rep. 13(1) (2023)","DOI":"10.1038\/s41598-022-26386-6"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Filho, E.S., et al.: A method for semi-automatic grading of human blastocyst microscope images. Hum.Reprod. 27(9), 2641\u20132648 (2012)","DOI":"10.1093\/humrep\/des219"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Hardarson, T., et\u00a0al.: The blastocyst. Human Reproduction 27(suppl_1), i72\u2013i91 (08 2012)","DOI":"10.1093\/humrep\/des230"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Harun, M.Y., et al.: Inner cell mass and trophectoderm segmentation in human blastocyst images using deep neural network. In: 2019 IEEE 13th International Conference on Nano\/Molecular Medicine & Engineering (NANOMED), pp. 214\u2013219 (2019)","DOI":"10.1109\/NANOMED49242.2019.9130618"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., et\u00a0al.: Inner cell mass segmentation in human HMC embryo images using fully convolutional network. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1752\u20131756 (2017)","DOI":"10.1109\/ICIP.2017.8296582"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., et al.: Human blastocyst segmentation using neural network. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1\u20134 (2016)","DOI":"10.1109\/CCECE.2016.7726763"},{"key":"22_CR9","unstructured":"Kovesi, P.: MatLab and octave functions for computer vision and image processing. https:\/\/www.peterkovesi.com\/matlabfns\/"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Marte\u00a0Myhre, R., Ritsa, S.: Development of in vitro fertilization, a very important part of human reproductive medicine, in the last 40 years. Int. J. Women\u2019s Health Wellness 5(1) (2019)","DOI":"10.23937\/2474-1353\/1510089"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Rad, R.M., et al.: Coarse-to-fine texture analysis for inner cell mass identification in human blastocyst microscopic images. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp.\u00a01\u20135 (2017)","DOI":"10.1109\/IPTA.2017.8310152"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.imu.2018.10.009","volume":"13","author":"RM Rad","year":"2018","unstructured":"Rad, R.M., et al.: Human blastocyst\u2019s zona pellucida segmentation via boosting ensemble of complementary learning. Inform. Med. Unlocked 13, 112\u2013121 (2018)","journal-title":"Inform. Med. Unlocked"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Rad, R.M., et al.: Multi-resolutional ensemble of stacked dilated U-Net for inner cell mass segmentation in human embryonic images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3518\u20133522 (2018)","DOI":"10.1109\/ICIP.2018.8451750"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Refaeilzadeh, P., et al.: Cross-Validation, pp. 532\u2013538. Springer, US, Boston, MA (2009)","DOI":"10.1007\/978-0-387-39940-9_565"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., et al.: U-Net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"22_CR16","doi-asserted-by":"publisher","first-page":"2968","DOI":"10.1109\/TBME.2017.2759665","volume":"64","author":"P Saeedi","year":"2017","unstructured":"Saeedi, P., et al.: Automatic identification of human blastocyst components via texture. IEEE Trans. Biomed. Eng. 64(12), 2968\u20132978 (2017). https:\/\/doi.org\/10.1109\/TBME.2017.2759665","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"4","key":"22_CR17","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.fertnstert.2012.01.104","volume":"97","author":"RT Scott","year":"2012","unstructured":"Scott, R.T., et al.: Comprehensive chromosome screening is highly predictive of the reproductive potential of human embryos: a prospective, blinded, nonselection study. Fertil. Steril. 97(4), 870\u2013875 (2012)","journal-title":"Fertil. Steril."},{"issue":"1","key":"22_CR18","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1109\/TBME.2014.2356415","volume":"62","author":"A Singh","year":"2015","unstructured":"Singh, A., et al.: Automatic segmentation of trophectoderm in microscopic images of human blastocysts. IEEE Trans. Biomed. Eng. 62(1), 382\u2013393 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"22_CR19","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/S0015-0282(16)55908-1","volume":"59","author":"JJ Tar\u00edn","year":"1993","unstructured":"Tar\u00edn, J.J., Handyside, A.H.: Embryo biopsy strategies for preimplantation diagnosis. Fertil. Steril. 59(5), 943\u2013952 (1993)","journal-title":"Fertil. Steril."},{"issue":"6337","key":"22_CR20","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1136\/bmj.285.6337.244","volume":"285","author":"A Trounson","year":"1982","unstructured":"Trounson, A., Conti, A.: Research in human in-vitro fertilisation and embryo transfer. BMJ 285(6337), 244\u2013248 (1982)","journal-title":"BMJ"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"VerMilyea, M., et al.: Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum. Reprod. 35(4), 770\u2013784 (2020)","DOI":"10.1093\/humrep\/deaa013"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: I2CNET: an intra- and inter-class context information fusion network for blastocyst segmentation. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 1415\u20131422. International Joint Conferences on Artificial Intelligence Organization (2022). main Track","DOI":"10.24963\/ijcai.2022\/197"},{"issue":"28","key":"22_CR23","doi-asserted-by":"publisher","first-page":"39829","DOI":"10.1007\/s11042-022-12696-4","volume":"81","author":"H Xia","year":"2022","unstructured":"Xia, H., et al.: HRNET: a hierarchical recurrent convolution neural network for retinal vessel segmentation. Multimed. Tools Appl. 81(28), 39829\u201339851 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12696-4","journal-title":"Multimed. Tools Appl."},{"key":"22_CR24","unstructured":"Yee, D., et al.: An automatic model-based approach for measuring the zona pellucida thickness in day five human blastocysts. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p. 1 (2013)"},{"issue":"11","key":"22_CR25","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.4103\/0366-6999.232808","volume":"131","author":"YY Zhao","year":"2018","unstructured":"Zhao, Y.Y., et al.: Overall blastocyst quality, trophectoderm grade, and inner cell mass grade predict pregnancy outcome in euploid blastocyst transfer cycles. Chin. Med. J. 131(11), 1261\u20131267 (2018)","journal-title":"Chin. Med. J."}],"container-title":["Lecture Notes in Computer Science","Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62799-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T11:39:24Z","timestamp":1718105964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62799-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031627989","9783031627996"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAEPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the Spanish Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"A Coru\u00f1a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caepia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caepia24.aepia.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}