{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:11:45Z","timestamp":1771067505024,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164392","type":"print"},{"value":"9783031164408","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_20","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Adaptive 3D Localization of\u00a02D Freehand Ultrasound Brain Images"],"prefix":"10.1007","author":[{"given":"Pak-Hei","family":"Yeung","sequence":"first","affiliation":[]},{"given":"Moska","family":"Aliasi","sequence":"additional","affiliation":[]},{"given":"Monique","family":"Haak","sequence":"additional","affiliation":[]},{"name":"the INTERGROWTH-21st Consortium","sequence":"additional","affiliation":[]},{"given":"Weidi","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Ana I. L.","family":"Namburete","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"11","key":"20_CR1","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","volume":"36","author":"CF Baumgartner","year":"2017","unstructured":"Baumgartner, C.F., et al.: SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204\u20132215 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR2","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"20_CR3","unstructured":"Chen, X., Wang, S., Wang, J., Long, M.: Representation subspace distance for domain adaptation regression. In: International Conference on Machine Learning, pp. 1749\u20131759. PMLR (2021)"},{"key":"20_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-030-59716-0_55","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Delaunay","year":"2020","unstructured":"Delaunay, R., Hu, Y., Vercauteren, T.: An unsupervised approach to ultrasound elastography with end-to-end strain regularisation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 573\u2013582. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_55"},{"key":"20_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-030-32254-0_33","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Dou","year":"2019","unstructured":"Dou, H., et al.: Agent with warm start and active termination for plane localization in 3D ultrasound. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 290\u2013298. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_33"},{"key":"20_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-030-59716-0_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Droste","year":"2020","unstructured":"Droste, R., Drukker, L., Papageorghiou, A.T., Noble, J.A.: Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 583\u2013592. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_56"},{"issue":"1","key":"20_CR7","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-030-60334-2_13","volume-title":"Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis","author":"Y Gao","year":"2020","unstructured":"Gao, Y., Beriwal, S., Craik, R., Papageorghiou, A.T., Noble, J.A.: Label efficient localization of fetal brain biometry planes in ultrasound through metric learning. In: Hu, Y., et al. (eds.) ASMUS\/PIPPI -2020. LNCS, vol. 12437, pp. 126\u2013135. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60334-2_13"},{"key":"20_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-319-66185-8_35","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"Y Gao","year":"2017","unstructured":"Gao, Y., Alison Noble, J.: Detection and characterization of the fetal heartbeat in free-hand ultrasound sweeps with weakly-supervised two-streams convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 305\u2013313. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_35"},{"issue":"12","key":"20_CR10","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.7863\/jum.2005.24.12.1599","volume":"24","author":"LF Gon\u00e7alves","year":"2005","unstructured":"Gon\u00e7alves, L.F., Lee, W., Espinoza, J., Romero, R.: Three- and 4-dimensional ultrasound in obstetric practice: does it help? J. Ultrasound Med. 24(12), 1599\u20131624 (2005)","journal-title":"J. Ultrasound Med."},{"key":"20_CR11","first-page":"21271","volume":"33","author":"JB Grill","year":"2020","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"20_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-319-66185-8_34","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"B Hou","year":"2017","unstructured":"Hou, B., et al.: Predicting slice-to-volume transformation in presence of arbitrary subject motion. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, Simon (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 296\u2013304. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_34"},{"issue":"8","key":"20_CR13","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/TMI.2018.2798801","volume":"37","author":"B Hou","year":"2018","unstructured":"Hou, B., et al.: 3-D reconstruction in canonical co-ordinate space from arbitrarily oriented 2-D images. IEEE Trans. Med. Imaging 37(8), 1737\u20131750 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1007\/978-3-030-59716-0_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"A K. Z. Tehrani","year":"2020","unstructured":"K. Z. Tehrani, A., Mirzaei, M., Rivaz, H.: Semi-supervised training of optical flow convolutional neural networks in ultrasound elastography. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 504\u2013513. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_48"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: Autonomous navigation of an ultrasound probe towards standard scan planes with deep reinforcement learning. In: 2021 IEEE International Conference on Robotics and Automation, pp. 8302\u20138308. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561295"},{"key":"20_CR16","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97\u2013105. PMLR (2015)"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Mohamed, F., Siang, C.V.: A survey on 3D ultrasound reconstruction techniques. In: Artificial Intelligence-Applications in Medicine and Biology, pp. 73\u201392 (2019)","DOI":"10.5772\/intechopen.81628"},{"issue":"10","key":"20_CR18","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1016\/j.ultrasmedbio.2017.06.009","volume":"43","author":"MH Mozaffari","year":"2017","unstructured":"Mozaffari, M.H., Lee, W.S.: Freehand 3-D ultrasound imaging: a systematic review. Ultrasound Med. Biol. 43(10), 2099\u20132124 (2017)","journal-title":"Ultrasound Med. Biol."},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2018.02.006","volume":"46","author":"AI Namburete","year":"2018","unstructured":"Namburete, A.I., Xie, W., Yaqub, M., Zisserman, A., Noble, J.A.: Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning. Med. Image Anal. 46, 1\u201314 (2018)","journal-title":"Med. Image Anal."},{"key":"20_CR20","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Paladini, D., Malinger, G., Monteagudo, A., Pilu, G., Timor-Tritsch, I., Toi, A.: Sonographic examination of the fetal central nervous system: guidelines for performing the \u2018basic examination\u2019 and the \u2018fetal neurosonogram.\u2019 Ultrasound Obstet. Gynecol. 29(1), 109\u2013116 (2007)","DOI":"10.1002\/uog.3909"},{"key":"20_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"20_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101998","volume":"70","author":"PH Yeung","year":"2021","unstructured":"Yeung, P.H., Aliasi, M., Papageorghiou, A.T., Haak, M., Xie, W., Namburete, A.I.: Learning to map 2D ultrasound images into 3D space with minimal human annotation. Med. Image Anal. 70, 101998 (2021)","journal-title":"Med. Image Anal."},{"key":"20_CR24","unstructured":"Yeung, P.H., et al.: ImplicitVol: sensorless 3D ultrasound reconstruction with deep implicit representation. arXiv preprint arXiv:2109.12108 (2021)"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., Efros, A.A.: Learning dense correspondence via 3D-guided cycle consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 117\u2013126 (2016)","DOI":"10.1109\/CVPR.2016.20"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:08:41Z","timestamp":1711562921000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/en\/","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}