{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:46:46Z","timestamp":1743079606012,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"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_11","type":"book-chapter","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:01:54Z","timestamp":1728450114000},"page":"119-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Automated Multi-regional Lung Parcellation for\u00a00.55-3T 3D T2w Fetal MRI"],"prefix":"10.1007","author":[{"given":"Alena U.","family":"Uus","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla Avena","family":"Zampieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fenella","family":"Downes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexia Egloff","family":"Collado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Megan","family":"Hall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Davidson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelly","family":"Payette","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jordina Aviles","family":"Verdera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irina","family":"Grigorescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph V.","family":"Hajnal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Deprez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Aertsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jana","family":"Hutter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary A.","family":"Rutherford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Deprest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Story","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1002\/pd.5579","volume":"40","author":"M Aertsen","year":"2020","unstructured":"Aertsen, M., et al.: Fetal MRI for dummies: what the fetal medicine specialist should know about acquisitions and sequences. Prenat. Diagn. 40, 6\u201317 (2020)","journal-title":"Prenat. Diagn."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Amodeo, I., et\u00a0al.: The role of MRI in the diagnosis and prognostic evaluation of fetuses with congenital diaphragmatic hernia, September 2022","DOI":"10.1007\/s00431-022-04540-6"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Cannie, M.M., et\u00a0al.: Fetal body volume at MR imaging to quantify total fetal lung volume: normal ranges. Radiology 247, 197\u2013203 (2008)","DOI":"10.1148\/radiol.2471070682"},{"key":"11_CR4","unstructured":"Cardoso, M.J., et\u00a0al.: Monai: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Conte, L., et\u00a0al.: Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with NNU-net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation. Eur. J. Pediat. 183, 2285\u20132300 (2024)","DOI":"10.1007\/s00431-024-05476-9"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Davidson, J., et\u00a0al.: Fetal body MRI and its application to fetal and neonatal treatment: an illustrative review. Lancet Child Adol. Health 5, 447\u2013458 (2021)","DOI":"10.1016\/S2352-4642(20)30313-8"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Davidson, J., et\u00a0al.: Motion corrected fetal body MRI provides reliable 3d lung volumes in normal and abnormal fetuses. Prenat. Diagn. 42, 628\u2013635 (2022)","DOI":"10.1002\/pd.6129"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Fujii, S., et\u00a0al.: The bronchial tree of the human embryo: an analysis of variations in the bronchial segments. J. Anatomy 237, 311\u2013322 (2020)","DOI":"10.1111\/joa.13199"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Jani, J., et\u00a0al.: Value of prenatal MRI in the prediction of postnatal outcome in fetuses with diaphragmatic hernia. UOG 32, 793\u2013799 (2008)","DOI":"10.1002\/uog.6234"},{"key":"11_CR10","first-page":"210","volume":"32","author":"B Lassen","year":"2013","unstructured":"Lassen, B., et al.: Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE TMI 32, 210\u2013222 (2013)","journal-title":"IEEE TMI"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Meyers, M.L., et\u00a0al.: Fetal lung volumes by MRI: normal weekly values from 18 through 38 weeks\u2019 gestation. Am. J. of Roentgenology 211, 432\u2013438 (2018)","DOI":"10.2214\/AJR.17.19469"},{"key":"11_CR12","unstructured":"Oktay, O., et\u00a0al.: Attention u-net: learning where to look for the pancreas. In: MIDDL 2016 (2018)"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Payette, K., et\u00a0al.: An automated pipeline for quantitative t2* fetal body MRI and segmentation at low field. In: MICCAI (2023)","DOI":"10.1007\/978-3-031-43990-2_34"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Perrone, E.E., et\u00a0al.: Prenatal assessment of congenital diaphragmatic hernia at north American fetal therapy network centers: a continued plea for standardization. Prenat. Diagn. 41, 200\u2013206 (2021)","DOI":"10.1002\/pd.5859"},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"4205","DOI":"10.1007\/s00330-022-09367-1","volume":"33","author":"F Prayer","year":"2023","unstructured":"Prayer, F., et al.: Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity. Eur. Radiol. 33, 4205\u20134213 (2023)","journal-title":"Eur. Radiol."},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Royston, P., Wright, E.: How to construct \u2018normal ranges\u2019 for fetal variables. Ultrasound Obstet. Gynecol. 11, 30\u201338 (1998)","DOI":"10.1046\/j.1469-0705.1998.11010030.x"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1148\/radiology.219.1.r01ap18236","volume":"219","author":"F Rypens","year":"2001","unstructured":"Rypens, F., et al.: Fetal lung volume: estimation at MR imaging-initial results. Radiology 219, 236\u201341 (2001)","journal-title":"Radiology"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Schittny, J.C.: Development of the lung. Cell Tissue R. 367, 427 (2017)","DOI":"10.1007\/s00441-016-2545-0"},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1038\/s41390-019-0717-9","volume":"87","author":"L Story","year":"2020","unstructured":"Story, L., et al.: Foetal lung volumes in pregnant women who deliver very preterm: a pilot study. Pediatr. Res. 87, 1066\u20131071 (2020)","journal-title":"Pediatr. Res."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Uus, A., et\u00a0al.: Deformable slice-to-volume registration for motion correction of fetal body and placenta MRI. IEEE TMI 39, 2750\u20132759 (2020)","DOI":"10.1109\/TMI.2020.2974844"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Uus, A.U., et\u00a0al.: Automated 3d reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21\u201336 weeks GA range. MedIAn 80, 102484 (2022)","DOI":"10.1016\/j.media.2022.102484"},{"issue":"1147","key":"11_CR22","doi-asserted-by":"publisher","first-page":"20220071","DOI":"10.1259\/bjr.20220071","volume":"96","author":"AU Uus","year":"2022","unstructured":"Uus, A.U., et al.: Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice. Br. J. Radiol. 96(1147), 20220071 (2022)","journal-title":"Br. J. Radiol."},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"6637","DOI":"10.1038\/s41598-024-57087-x","volume":"14","author":"AU Uus","year":"2024","unstructured":"Uus, A.U., et al.: Automated body organ segmentation, volumetry and population-averaged atlas for 3d motion-corrected t2-weighted fetal body mri. Sci. Rep. 14, 6637 (2024)","journal-title":"Sci. Rep."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Vimala, V., et\u00a0al.: Morphological study of fissure and lobes in fetal lungs. Int. J. Anat. Res. 6, 4959\u20134962 (2018)","DOI":"10.16965\/ijar.2017.523"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Wasserthal, J., et\u00a0al.: Totalsegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5, e230024 (2023)","DOI":"10.1148\/ryai.230024"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Whitby, E., Gaunt, T.: Fetal lung MRI and features predicting post-natal outcome: a scoping review of the current literature (2023)","DOI":"10.1259\/bjr.20220344"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Wilson, L., Whitby, E.H.: MRI prediction of fetal lung volumes and the impact on counselling. Clin. Radiol. 78, 955\u2013959 (2023)","DOI":"10.1016\/j.crad.2023.09.006"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:02:55Z","timestamp":1728450175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73260-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"ISBN":["9783031732591","9783031732607"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73260-7_11","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":"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"}}]}}