{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:09:36Z","timestamp":1772302176579,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030880095","type":"print"},{"value":"9783030880101","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88010-1_28","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:06:25Z","timestamp":1634857585000},"page":"337-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Semantic Edge for Cell Counting and Localization in Time-Lapse Microscopy Images"],"prefix":"10.1007","author":[{"given":"Tianwei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.patcog.2012.09.020","volume":"46","author":"C Akinlar","year":"2013","unstructured":"Akinlar, C., Topal, C.: Edcircles: a real-time circle detector with a false detection control. Pattern Recognit. 46, 725\u2013740 (2013)","journal-title":"Pattern Recognit."},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Aprinaldi, Habibie, I., Rahmatullah, R., Kurniawan, A., Bowolaksono, A., Jatmiko, W., Wiweko, B.: Arcpso: ellipse detection method using particle swarm optimization and arc combination. In: International Conference on Advanced Computer Science and Information System, pp. 408\u2013413 (2014)","DOI":"10.1109\/ICACSIS.2014.7065877"},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"JF Canny","year":"1986","unstructured":"Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2014.04.011","volume":"51","author":"M Cicconet","year":"2014","unstructured":"Cicconet, M., Gutwein, M., Gunsalus, K.C., Geiger, D.: Label free cell-tracking and division detection based on 2d time-lapse images for lineage analysis of early embryo development. Comput. Biol. Med. 51, 24\u201334 (2014)","journal-title":"Comput. Biol. Med."},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/978-3-030-01231-1_35","volume-title":"Computer Vision","author":"R Deng","year":"2018","unstructured":"Deng, R., Shen, C., Liu, S., Wang, H., Liu, X.: Learning to Predict Crisp Boundaries. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 570\u2013586. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_35"},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"3693","DOI":"10.1016\/j.patcog.2014.05.012","volume":"47","author":"M Fornaciari","year":"2014","unstructured":"Fornaciari, M., Prati, A., Cucchiara, R.: A fast and effective ellipse detector for embedded vision applications. Pattern Recogn. 47, 3693\u20133708 (2014)","journal-title":"Pattern Recogn."},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Giusti, A., Corani, G., Gambardella, L.M., Magli, C., Gianaroli, L.: Blastomere segmentation and 3d morphology measurements of early embryos from hoffman modulation contrast image stacks. In: International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1261\u20131264. IEEE (2010)","DOI":"10.1109\/ISBI.2010.5490225"},{"issue":"1","key":"28_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s00138-017-0880-0","volume":"29","author":"A Grushnikov","year":"2018","unstructured":"Grushnikov, A., Niwayama, R., Kanade, T., Yagi, Y.: 3d level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images. Mach. Vis. Appl. 29(1), 125\u2013134 (2018)","journal-title":"Mach. Vis. Appl."},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., Salzmann, M.: Automated monitoring of human embryonic cells up to the 5-cell stage in time-lapse microscopy images. In: International Symposium on Biomedical Imaging, pp. 389\u2013393. IEEE (2015)","DOI":"10.1109\/ISBI.2015.7163894"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., Salzmann, M.: A linear chain markov model for detection and localization of cells in early stage embryo development. In: Winter Conference on Applications of Computer Vision, pp. 526\u2013533. IEEE Computer Society (2015)","DOI":"10.1109\/WACV.2015.76"},{"key":"28_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-3-319-46604-0_25","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"A Khan","year":"2016","unstructured":"Khan, A., Gould, S., Salzmann, M.: Deep convolutional neural networks for human embryonic cell counting. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 339\u2013348. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46604-0_25"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., Salzmann, M.: Segmentation of developing human embryo in time-lapse microscopy. In: International Symposium on Biomedical Imaging, pp. 930\u2013934. IEEE (2016)","DOI":"10.1109\/ISBI.2016.7493417"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M., Hu, X., Wang, K., Bai, X.: Richer convolutional features for edge detection. In: Conference on Computer Vision and Pattern Recognition, pp. 5872\u20135881. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.622"},{"key":"28_CR14","doi-asserted-by":"publisher","first-page":"122153","DOI":"10.1109\/ACCESS.2019.2937765","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu, Z., et al.: Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos. IEEE Access 7, 122153\u2013122163 (2019)","journal-title":"IEEE Access"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Lu, C., Xia, S., Huang, W., Shao, M., Fu, Y.: Circle detection by arc-support line segments. In: International Conference on Image Processing, pp. 76\u201380. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296246"},{"key":"28_CR16","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/TIP.2019.2934352","volume":"29","author":"C Lu","year":"2020","unstructured":"Lu, C., Xia, S., Shao, M., Fu, Y.: Arc-support line segments revisited: an efficient high-quality ellipse detection. IEEE Trans. Image Process. 29, 768\u2013781 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Malmsten, J., Zaninovic, N., Zhan, Q., Rosenwaks, Z., Shan, J.: Automated cell stage predictions in early mouse and human embryos using convolutional neural networks. In: International Conference on Biomedical & Health Informatics, pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/BHI.2019.8834541"},{"key":"28_CR18","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/0005-1098(75)90044-8","volume":"11","author":"N Otsu","year":"1975","unstructured":"Otsu, N.: A threshold selection method from gray-level histogram. Automatica 11, 285\u2013296 (1975)","journal-title":"Automatica"},{"key":"28_CR19","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.compbiomed.2018.08.001","volume":"101","author":"RM Rad","year":"2018","unstructured":"Rad, R.M., Saeedi, P., Au, J., Havelock, J.: A hybrid approach for multiple blastomeres identification in early human embryo images. Comput. Biol. Med. 101, 100\u2013111 (2018)","journal-title":"Comput. Biol. Med."},{"key":"28_CR20","doi-asserted-by":"publisher","first-page":"81945","DOI":"10.1109\/ACCESS.2019.2920933","volume":"7","author":"RM Rad","year":"2019","unstructured":"Rad, R.M., Saeedi, P., Au, J., Havelock, J.: Cell-net: embryonic cell counting and centroid localization via residual incremental atrous pyramid and progressive upsampling convolution. IEEE Access 7, 81945\u201381955 (2019)","journal-title":"IEEE Access"},{"key":"28_CR21","doi-asserted-by":"publisher","first-page":"101612","DOI":"10.1016\/j.media.2019.101612","volume":"62","author":"RM Rad","year":"2020","unstructured":"Rad, R.M., Saeedi, P., Au, J., Havelock, J.: Trophectoderm segmentation in human embryo images via inceptioned u-net. Med. Image Anal. 62, 101612 (2020)","journal-title":"Med. Image Anal."},{"key":"28_CR22","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Singh, A., Buonassisi, J., Saeedi, P., Havelock, J.: Automatic blastomere detection in day 1 to day 2 human embryo images using partitioned graphs and ellipsoids. In: International Conference on Image Processing, pp. 917\u2013921. IEEE (2014)","DOI":"10.1109\/ICIP.2014.7025184"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Soria, X., Riba, E., Sappa, \u00c1.D.: Dense extreme inception network: Towards a robust CNN model for edge detection. In: Winter Conference on Applications of Computer Vision, pp. 1912\u20131921. IEEE (2020)","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"28_CR25","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cmpb.2017.12.022","volume":"156","author":"C Strouthopoulos","year":"2018","unstructured":"Strouthopoulos, C., Anifandis, G.: An automated blastomere identification method for the evaluation of day 2 embryos during IVF\/ICSI treatments. Comput. Methods Programs Biomed. 156, 53\u201359 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Syulistyo, A.R., Aprinaldi, Bowolaksono, A., Wiweko, B., Prati, A., Purnomo, D.M.J., Jatmiko, W.: Ellipse detection on embryo imaging using Random Sample Consensus (RANSAC) method based on arc segment. Int. J. Smart Sens. Intell. Syst. 9(3), 1384\u20131409 (2016)","DOI":"10.21307\/ijssis-2017-923"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Syulistyo, A.R., Wisesa, H.A., Aprinaldi, Bowolaksono, A., Wiweko, B., Jatmiko, W.: Ellipse detection on embryo image using modification of arc particle swarm optimization (arcpso) based arc segment. In: International Symposium on Micro-Nano Mechatronics and Human Science, pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/MHS.2015.7438307"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Tian, Y., Yin, Y., Duan, F., Wang, W., Wang, W., Zhou, M.: Automatic blastomere recognition from a single embryo image. Comput. Math. Methods Med. 2014, 628312:1\u2013628312:7 (2014)","DOI":"10.1155\/2014\/628312"},{"key":"28_CR29","doi-asserted-by":"publisher","first-page":"101726","DOI":"10.1016\/j.bspc.2019.101726","volume":"57","author":"MM Trivedi","year":"2020","unstructured":"Trivedi, M.M., Mills, J.K.: Centroid calculation of the blastomere from 3d z-stack image data of a 2-cell mouse embryo. Biomed. Signal Process. Control. 57, 101726 (2020)","journal-title":"Biomed. Signal Process. Control."},{"key":"28_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-642-40763-5_57","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"Yu Wang","year":"2013","unstructured":"Wang, Yu., Moussavi, F., Lorenzen, P.: Automated embryo stage classification in time-lapse microscopy video of early human embryo development. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 460\u2013467. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_57"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: International Conference on Computer Vision, pp. 1395\u20131403. IEEE Computer Society (2015)","DOI":"10.1109\/ICCV.2015.164"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88010-1_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T08:06:25Z","timestamp":1654502785000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88010-1_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880095","9783030880101"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88010-1_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/index_en.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"513","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"201","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"There were 30 oral and 171 poster presentations at the conference.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}