{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:19:07Z","timestamp":1776089947021,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872335","type":"print"},{"value":"9783030872342","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\/978-3-030-87234-2_22","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"228-238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes"],"prefix":"10.1007","author":[{"given":"Sophia","family":"Bano","sequence":"first","affiliation":[]},{"given":"Brian","family":"Dromey","sequence":"additional","affiliation":[]},{"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[]},{"given":"Raffaele","family":"Napolitano","sequence":"additional","affiliation":[]},{"given":"Anna L.","family":"David","sequence":"additional","affiliation":[]},{"given":"Donald M.","family":"Peebles","sequence":"additional","affiliation":[]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"11","key":"22_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"},{"issue":"3","key":"22_CR2","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1002\/uog.18811","volume":"52","author":"A Cavallaro","year":"2018","unstructured":"Cavallaro, A., et al.: Quality control of ultrasound for fetal biometry: results from the intergrowth-21st project. Ultrasound Obstetrics Gynecol. 52(3), 332\u2013339 (2018)","journal-title":"Ultrasound Obstetrics Gynecol."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"526","DOI":"10.3389\/fneur.2020.00526","volume":"11","author":"X Chen","year":"2020","unstructured":"Chen, X., et al.: Automatic measurements of fetal lateral ventricles in 2d ultrasound images using deep learning. Front. Neurol. 11, 526 (2020)","journal-title":"Front. Neurol."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Dromey, B.P., et al.: Dimensionless squared jerk: an objective differential to assess experienced and novice probe movement in obstetric ultrasound. Prenat. Diagn. 41(2), 271\u2013277 (2020)","DOI":"10.1002\/pd.5855"},{"key":"22_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"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia. MM \u201919, ACM, New York, NY, USA (2019)","DOI":"10.1145\/3343031.3350535"},{"issue":"5","key":"22_CR8","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/34.765658","volume":"21","author":"A Fitzgibbon","year":"1999","unstructured":"Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476\u2013480 (1999)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR9","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"},{"issue":"11","key":"22_CR10","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.diii.2018.08.001","volume":"99","author":"GA Grandjean","year":"2018","unstructured":"Grandjean, G.A., Hossu, G., Bertholdt, C., Noble, P., Morel, O., Grang\u00e9, G.: Artificial intelligence assistance for fetal head biometry: assessment of automated measurement software. Diagn. Intervent. Imaging 99(11), 709\u2013716 (2018)","journal-title":"Diagn. Intervent. Imaging"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Hermawati, F., Tjandrasa, H., Sari, G.P., Azis, A., et al.: Automatic femur length measurement for fetal ultrasound image using localizing region-based active contour method. In: Journal of Physics: Conference Series, vol. 1230, p. 012002. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1230\/1\/012002"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"van den Heuvel, T.L., de Bruijn, D., de Korte, C.L., Ginneken, B.V.: Automated measurement of fetal head circumference using 2D ultrasound images. PLOS One 13(8), e0200412 (2018)","DOI":"10.1371\/journal.pone.0200412"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Khan, N.H., Tegnander, E., Dreier, J.M., Eik-Nes, S., Torp, H., Kiss, G.: Automatic measurement of the fetal abdominal section on a portable ultrasound machine for use in low and middle income countries. In: 2016 IEEE International Ultrasonics Symposium (IUS), pp. 1\u20134. IEEE (2016)","DOI":"10.1109\/ULTSYM.2016.7728557"},{"issue":"3","key":"22_CR15","doi-asserted-by":"publisher","first-page":"334","DOI":"10.4236\/ojog.2017.73035","volume":"7","author":"NH Khan","year":"2017","unstructured":"Khan, N.H., Tegnander, E., Dreier, J.M., Eik-Nes, S., Torp, H., Kiss, G.: Automatic detection and measurement of fetal biparietal diameter and femur length\u2013feasibility on a portable ultrasound device. Open J. Obstet. Gynecol. 7(3), 334\u2013350 (2017)","journal-title":"Open J. Obstet. Gynecol."},{"issue":"1","key":"22_CR16","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/JBHI.2017.2703890","volume":"22","author":"J Li","year":"2017","unstructured":"Li, J., et al.: Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J. Biomed. Health Inform. 22(1), 215\u2013223 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Z., et al.: Multi-task learning for quality assessment of fetal head ultrasound images. Med. Image Anal. 58, 101548 (2019)","DOI":"10.1016\/j.media.2019.101548"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"22_CR19","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TMI.2013.2276943","volume":"33","author":"S Rueda","year":"2013","unstructured":"Rueda, S., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33(4), 797\u2013813 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Salomon, L., et al.: ISUOG practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstetrics Gynecol. 53(6), 715\u2013723 (2019)","DOI":"10.1002\/uog.20272"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Sarris, I., Ioannou, C., Chamberlain, P., Ohuma, E., Roseman, F., Hoch, L., Altman, D., Papageorghiou, A., International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st): Intra-and interobserver variability in fetal ultrasound measurements. Ultrasound Obstet. Gynecol. 39(3), 266\u2013273 (2012)","DOI":"10.1002\/uog.10082"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Sobhaninia, Z., et al.: Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6545\u20136548. IEEE (2019)","DOI":"10.1109\/EMBC.2019.8856981"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dudley, N.J., Lambrou, T., Allinson, N., Ye, X.: Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image. J. Med. Imaging 4(2), 024001 (2017)","DOI":"10.1117\/1.JMI.4.2.024001"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87234-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T05:11:06Z","timestamp":1651641066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87234-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872335","9783030872342"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87234-2_22","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":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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":"The conference was held virtually.","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)"}}]}}