{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:40:28Z","timestamp":1753602028990,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732591"},{"type":"electronic","value":"9783031732607"}],"license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73260-7_12","type":"book-chapter","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:01:54Z","timestamp":1728450114000},"page":"130-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fetal Body Parts Segmentation Using Volumetric MRI Reconstructions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6033-0851","authenticated-orcid":false,"given":"Pedro Pablo","family":"Alarc\u00f3n-Gil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-3838","authenticated-orcid":false,"given":"Felicia","family":"Alfano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5796-2145","authenticated-orcid":false,"given":"Alena","family":"Uus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-3923","authenticated-orcid":false,"given":"Mar\u00eda Jes\u00fas","family":"Ledesma-Carbayo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1477-304X","authenticated-orcid":false,"given":"Lucilio","family":"Cordero-Grande","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"issue":"2","key":"12_CR1","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s00247-022-05495-4","volume":"53","author":"G Papaioannou","year":"2023","unstructured":"Papaioannou, G., Caro-Dom\u00ednguez, P., Klein, W.M., Garel, C., Cassart, M.: Indications for magnetic resonance imaging of the fetal body (extra-central nervous system): recommendations from the European Society of Paediatric Radiology Fetal Task Force. Pediatr. Radiol. 53(2), 297\u2013312 (2023). https:\/\/doi.org\/10.1007\/s00247-022-05495-4","journal-title":"Pediatr. Radiol."},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Wilson, L., Whitby, E.H.: The value of fetal magnetic resonance imaging in diagnosis of congenital anomalies of the fetal body: a systematic review and meta-analysis. BMC Med. Imag. 24(1) (2024). https:\/\/doi.org\/10.1186\/s12880-024-01286-5","DOI":"10.1186\/s12880-024-01286-5"},{"issue":"9","key":"12_CR3","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.1109\/tmi.2020.2974844","volume":"39","author":"A Uus","year":"2020","unstructured":"Uus, A., et al.: Deformable slice-to-volume registration for motion correction of fetal body and placenta MRI. IEEE Trans. Med. Imaging 39(9), 2750\u20132759 (2020). https:\/\/doi.org\/10.1109\/tmi.2020.2974844","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR4","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.placenta.2023.08.066","volume":"142","author":"N Mufti","year":"2023","unstructured":"Mufti, N., et al.: Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: case series. Placenta 142, 36\u201345 (2023). https:\/\/doi.org\/10.1016\/j.placenta.2023.08.066","journal-title":"Placenta"},{"issue":"3","key":"12_CR5","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/tmi.2022.3217725","volume":"42","author":"L Cordero-Grande","year":"2023","unstructured":"Cordero-Grande, L., et al.: Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration. IEEE Trans. Med. Imaging 42(3), 810\u2013822 (2023). https:\/\/doi.org\/10.1109\/tmi.2022.3217725","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Uus, A.U., et\u00a0al.: Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI. Sci. Rep. 14(1) (2024). https:\/\/doi.org\/10.1038\/s41598-024-57087-x","DOI":"10.1038\/s41598-024-57087-x"},{"issue":"6","key":"12_CR7","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s00246-022-03038-0","volume":"44","author":"D Ryd","year":"2023","unstructured":"Ryd, D., Nilsson, A., Heiberg, E., Hedstr\u00f6m, E.: Automatic segmentation of the fetus in 3D magnetic resonance images using deep learning: accurate and fast fetal volume quantification for clinical use. Pediatr. Cardiol. 44(6), 1311\u20131318 (2023). https:\/\/doi.org\/10.1007\/s00246-022-03038-0","journal-title":"Pediatr. Cardiol."},{"issue":"3","key":"12_CR8","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1007\/s00330-023-10038-y","volume":"34","author":"B Specktor-Fadida","year":"2024","unstructured":"Specktor-Fadida, B., et al.: Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility. Eur. Radiol. 34(3), 2072\u20132083 (2024). https:\/\/doi.org\/10.1007\/s00330-023-10038-y","journal-title":"Eur. Radiol."},{"issue":"2","key":"12_CR9","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1002\/jmri.29141","volume":"60","author":"A Rabinowich","year":"2024","unstructured":"Rabinowich, A., et al.: Fetal MRI-based body and adiposity quantification for small for gestational age perinatal risk stratification. J. Magn. Reson. Imaging 60(2), 767\u2013774 (2024). https:\/\/doi.org\/10.1002\/jmri.29141","journal-title":"J. Magn. Reson. Imaging"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Hall, M., et\u00a0al.: Adrenal volumes in fetuses delivering prior to 32.weeks\u2019 gestation: an MRI pilot study. Acta Obstetricia et Gynecologica Scandinavica 103(3), 512\u2013521 (2024). https:\/\/doi.org\/10.1111\/aogs.14733","DOI":"10.1111\/aogs.14733"},{"key":"12_CR11","unstructured":"Zhang, T., et\u00a0al.: Graph-based whole body segmentation in fetal MR images. In: MICCAI Workshop PIPPI (2016). https:\/\/pippiworkshop.github.io\/pippi2016\/pdf\/PIPPI2016_04_Zhang.pdf"},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.placenta.2023.02.009","volume":"134","author":"CPS Kulseng","year":"2023","unstructured":"Kulseng, C.P.S., Hillestad, V., Eskild, A., Gjesdal, K.I.: Automatic placental and fetal volume estimation by a convolutional neural network. Placenta 134, 23\u201329 (2023). https:\/\/doi.org\/10.1016\/j.placenta.2023.02.009","journal-title":"Placenta"},{"key":"12_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-030-59725-2_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"G Dudovitch","year":"2020","unstructured":"Dudovitch, G., Link-Sourani, D., Ben Sira, L., Miller, E., Ben Bashat, D., Joskowicz, L.: Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 365\u2013374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_35"},{"key":"12_CR14","doi-asserted-by":"publisher","unstructured":"Lo, J., et\u00a0al.: Cross attention squeeze excitation network (CASE-Net) for whole body fetal MRI segmentation. Sensors 21(13) (2021). https:\/\/doi.org\/10.3390\/s21134490","DOI":"10.3390\/s21134490"},{"key":"12_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-031-16760-7_2","volume-title":"Medical Image Learning with Limited and Noisy Data","author":"BS Fadida","year":"2022","unstructured":"Fadida, B.S., Sourani, D.L., Sira, L.B., Miller, E., Bashat, D.B., Joskowicz, L.: Partial annotations for the segmentation of large structures with low annotation cost. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds.) MILLanD 2022. LNCS, vol. 13559, pp. 13\u201322. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16760-7_2"},{"key":"12_CR16","unstructured":"iFIND: Intelligent fetal imaging and diagnosis. http:\/\/www.ifindproject.com"},{"issue":"2","key":"12_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat. Methods"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Isensee, F., et\u00a0al.: nnU-Net revisited: a call for rigorous validation in 3D medical image segmentation (2024). https:\/\/doi.org\/10.48550\/arXiv.2404.09556","DOI":"10.48550\/arXiv.2404.09556"},{"issue":"8898","key":"12_CR19","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/s0140-6736(94)92638-7","volume":"343","author":"P Baker","year":"1994","unstructured":"Baker, P., et al.: Fetal weight estimation by echo-planar magnetic resonance imaging. Lancet 343(8898), 644\u2013645 (1994). https:\/\/doi.org\/10.1016\/s0140-6736(94)92638-7","journal-title":"Lancet"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Liu, R., et\u00a0al.: An intriguing failing of convolutional neural networks and the CoordConv solution, July 2018. https:\/\/doi.org\/10.48550\/arXiv.1807.03247","DOI":"10.48550\/arXiv.1807.03247"}],"container-title":["Lecture Notes in Computer Science","Perinatal, Preterm and Paediatric Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73260-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:03:17Z","timestamp":1728450197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73260-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"ISBN":["9783031732591","9783031732607"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73260-7_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"10 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pippi2024","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"}}]}}