{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:39:06Z","timestamp":1761061146991,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030603335"},{"type":"electronic","value":"9783030603342"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-60334-2_20","type":"book-chapter","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T19:05:43Z","timestamp":1601492743000},"page":"201-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["3D Fetal Pose Estimation with Adaptive Variance and Conditional Generative Adversarial Network"],"prefix":"10.1007","author":[{"given":"Junshen","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Molin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esra Abaci","family":"Turk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. Ellen","family":"Grant","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Polina","family":"Golland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elfar","family":"Adalsteinsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"issue":"2","key":"20_CR1","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/0029-7844(95)00386-X","volume":"87","author":"GR Alexander","year":"1996","unstructured":"Alexander, G.R., Himes, J.H., Kaufman, R.B., Mor, J., Kogan, M.: A united states national reference for fetal growth. Obstet. Gynecol. 87(2), 163\u2013168 (1996)","journal-title":"Obstet. Gynecol."},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pp. 3686\u20133693 (2014)","DOI":"10.1109\/CVPR.2014.471"},{"key":"20_CR3","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)"},{"issue":"11","key":"20_CR4","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1088\/0967-3334\/37\/11\/2003","volume":"37","author":"H Biglari","year":"2016","unstructured":"Biglari, H., Sameni, R.: Fetal motion estimation from noninvasive cardiac signal recordings. Physiol. Meas. 37(11), 2003 (2016)","journal-title":"Physiol. Meas."},{"key":"20_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"issue":"3","key":"20_CR6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1111\/j.1447-0756.1996.tb00982.x","volume":"22","author":"FY Fai","year":"1996","unstructured":"Fai, F.Y., Singh, K., Malcus, P., Biswas, A., Arulkumaran, S., Ratnam, S.: Assessment of fetal health should be based on maternal perception of clusters rather than episodes of fetal movements. J. Obstet. Gynaecol. Res. 22(3), 299\u2013304 (1996)","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"20_CR7","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"issue":"2","key":"20_CR8","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/01443610801912618","volume":"28","author":"AP Heazell","year":"2008","unstructured":"Heazell, A.P., Fr\u00f8en, J.: Methods of fetal movement counting and the detection of fetal compromise. J. Obstet. Gynaecol. 28(2), 147\u2013154 (2008)","journal-title":"J. Obstet. Gynaecol."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"3","key":"20_CR10","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1002\/1522-2594(200103)45:3<486::AID-MRM1064>3.0.CO;2-#","volume":"45","author":"L Itti","year":"2001","unstructured":"Itti, L., Chang, L., Ernst, T.: Automatic scan prescription for brain MRI. Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med. 45(3), 486\u2013494 (2001)","journal-title":"Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med."},{"issue":"1","key":"20_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.1469-8749.2010.03813.x","volume":"53","author":"RP Jokhi","year":"2011","unstructured":"Jokhi, R.P., Whitby, E.H.: Magnetic resonance imaging of the fetus. Dev. Med. Child Neurol. 53(1), 18\u201328 (2011)","journal-title":"Dev. Med. Child Neurol."},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Khan, N.U., Wan, W.: A review of human pose estimation from single image. In: 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp. 230\u2013236. IEEE (2018)","DOI":"10.1109\/ICALIP.2018.8455796"},{"key":"20_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"20_CR14","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"1","key":"20_CR15","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1038\/s41598-017-03450-0","volume":"7","author":"J Luo","year":"2017","unstructured":"Luo, J., et al.: In vivo quantification of placental insufficiency by bold MRI: a human study. Sci. Rep. 7(1), 3713 (2017)","journal-title":"Sci. Rep."},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"issue":"3","key":"20_CR17","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1111\/j.1447-0756.2007.00524.x","volume":"33","author":"K Matsuo","year":"2007","unstructured":"Matsuo, K., Shimoya, K., Ushioda, N., Kimura, T.: Maternal positioning and fetal positioning in utero. J. Obstet. Gynaecol. Res. 33(3), 279\u2013282 (2007)","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"20_CR18","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"20_CR19","series-title":"Lecture Notes in Computer ScienceLecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","volume":"54","author":"C Payer","year":"2019","unstructured":"Payer, C., \u0160tern, D., Bischof, H., Urschler, M.: Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Med. Image Anal. 54, 207\u2013219 (2019)","journal-title":"Med. Image Anal."},{"issue":"5","key":"20_CR21","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.jare.2013.06.001","volume":"5","author":"SN Saleem","year":"2014","unstructured":"Saleem, S.N.: Fetal MRI: an approach to practice: a review. J. Adv. Res. 5(5), 507\u2013523 (2014)","journal-title":"J. Adv. Res."},{"key":"20_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-030-00889-5_22","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"N Toussaint","year":"2018","unstructured":"Toussaint, N., et al.: Weakly supervised localisation for fetal ultrasound images. In: Stoyanov, D., et al. (eds.) DLMIA 2018, ML-CDS 2018. LNCS, vol. 11045, pp. 192\u2013200. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_22"},{"key":"20_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-030-32251-9_44","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Xu","year":"2019","unstructured":"Xu, J., et al.: Fetal pose estimation in volumetric MRI using a 3D convolution neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 403\u2013410. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_44"},{"key":"20_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-3-030-32254-0_32","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"X Yang","year":"2019","unstructured":"Yang, X., et al.: FetusMap: fetal pose estimation in 3D ultrasound. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 281\u2013289. Springer, Cham (2019)"},{"issue":"1","key":"20_CR25","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s00247-018-4254-1","volume":"49","author":"CJ Yen","year":"2019","unstructured":"Yen, C.J., Mehollin-Ray, A.R., Bernardo, F., Zhang, W., Cassady, C.I.: Correlation between maternal meal and fetal motion during fetal MRI. Pediatr. Radiol. 49(1), 46\u201350 (2019)","journal-title":"Pediatr. Radiol."}],"container-title":["Lecture Notes in Computer Science","Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60334-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:12:06Z","timestamp":1759270326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60334-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030603335","9783030603342"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60334-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PIPPI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Preterm, Perinatal and Paediatric Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pippi2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pippiworkshop.github.io\/","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":"21","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":"14","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":"67% - 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":"2,4","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":"1,6","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 due to the Coronavirus pandemic.","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)"}}]}}