{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:01:58Z","timestamp":1776225718582,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032144164","type":"print"},{"value":"9783032144171","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-14417-1_5","type":"book-chapter","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T17:21:32Z","timestamp":1769102492000},"page":"50-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmenting Infant Brains Across Magnetic Fields: Domain Randomization and\u00a0Annotation Curation in\u00a0Ultra-low Field MRI"],"prefix":"10.1007","author":[{"given":"Vladyslav","family":"Zalevskyi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dondu-Busra","family":"Bulut","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meritxell","family":"Bach Cuadra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.pnpbp.2013.09.010","volume":"48","author":"N Barnea-Goraly","year":"2014","unstructured":"Barnea-Goraly, N., et al.: A preliminary longitudinal volumetric mri study of amygdala and hippocampal volumes in autism. Prog. Neuropsychopharmacol. Biol. Psychiatry 48, 124\u2013128 (2014)","journal-title":"Prog. Neuropsychopharmacol. Biol. Psychiatry"},{"issue":"4","key":"5_CR2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/S2215-0366(17)30049-4","volume":"4","author":"M Hoogman","year":"2017","unstructured":"Hoogman, M., et al.: Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4(4), 310\u2013319 (2017)","journal-title":"Lancet Psychiatry"},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.jocn.2020.03.049","volume":"78","author":"Q Xu","year":"2020","unstructured":"Xu, Q., Zuo, C., Liao, S., Long, Y., Wang, Y.: Abnormal development pattern of the amygdala and hippocampus from childhood to adulthood with autism. J. Clin. Neurosci. 78, 327\u2013332 (2020)","journal-title":"J. Clin. Neurosci."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Lepore, N., et al.: Low field pediatric brain magnetic resonance image segmentation and quality assurance (lisa) (2025). https:\/\/doi.org\/10.5281\/zenodo.15081583","DOI":"10.1007\/978-3-031-83008-2"},{"issue":"12","key":"5_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.5022","volume":"36","author":"M Jalloul","year":"2023","unstructured":"Jalloul, M., et al.: Mri scarcity in low-and middle-income countries. NMR Biomed. 36(12), e5022 (2023)","journal-title":"NMR Biomed."},{"issue":"7","key":"5_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4992","volume":"37","author":"S Murali","year":"2024","unstructured":"Murali, S., et al.: Bringing mri to low-and middle-income countries: directions, challenges and potential solutions. NMR Biomed. 37(7), e4992 (2024)","journal-title":"NMR Biomed."},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Iglesias, J.E., et al.: Quantitative brain morphometry of portable low-field-strength mri using super-resolution machine learning. Radiology, 306(3) (2023). ISSN 1527-1315. https:\/\/doi.org\/10.1148\/radiol.220522","DOI":"10.1148\/radiol.220522"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: Whole-body magnetic resonance imaging at 0.05 tesla. Science, 384(6696), eadm7168 (2024)","DOI":"10.1126\/science.adm7168"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Johnson, I., et al.: Automated segmentation of white matter hyperintensities on portable low-field magnetic resonance imaging (p9-13.002). Neurology, 104 (2025). ISSN 1526-632X","DOI":"10.1212\/WNL.0000000000208639"},{"key":"5_CR10","unstructured":"Gopinath, K., et\u00a0al. From low field to high value: robust cortical mapping from low-field mri. arXiv preprint arXiv:2505.12228 (2025)"},{"key":"5_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"B Billot","year":"2023","unstructured":"Billot, B., et al.: Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining. Med. Image Anal. 86, 102789 (2023)","journal-title":"Med. Image Anal."},{"issue":"9","key":"5_CR12","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2216399120","volume":"120","author":"B Billot","year":"2023","unstructured":"Billot, B., Magdamo, C., Cheng, Y., Arnold, S.E., Das, S., Iglesias, J.E.: Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain mri datasets. Proc. National Academy Sci. 120(9), e2216399120 (2023)","journal-title":"Proc. National Academy Sci."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"V\u00e1\u0161a, F., et\u00a0al. Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI. BioRxiv, 2024\u201308 (2024)","DOI":"10.1101\/2024.08.14.607942"},{"issue":"6","key":"5_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26674","volume":"45","author":"R Valabregue","year":"2024","unstructured":"Valabregue, R., Girka, F., Pron, A., Rousseau, F., Auzias, G.: Comprehensive analysis of synthetic learning applied to neonatal brain mri segmentation. Human Brain Mapp. 45(6), e26674 (2024)","journal-title":"Human Brain Mapp."},{"key":"5_CR15","unstructured":"Zalevskyi, V., et al.: Maximizing domain generalization in fetal brain tissue segmentation: the role of synthetic data generation, intensity clustering and real image fine-tuning. arXiv preprint arXiv:2411.06842 (2024)"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"A.\u00a0David Edwards, et al.: The developing human connectome project neonatal data release. Front. Neurosci., 16 (2022). ISSN 1662-453X. https:\/\/doi.org\/10.3389\/fnins.2022.886772","DOI":"10.3389\/fnins.2022.886772"},{"key":"5_CR17","doi-asserted-by":"publisher","DOI":"10.1101\/2024.10.02.616147","author":"E Feczko","year":"2024","unstructured":"Feczko, E., et al.: Baby open brains: an open-source repository of infant brain segmentations (2024). https:\/\/doi.org\/10.1101\/2024.10.02.616147","journal-title":"Baby open brains: an open-source repository of infant brain segmentations"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Feczko, E., et\u00a0al.: Baby open brains: an open-source repository of infant brain segmentations. bioRxiv (2024)","DOI":"10.1101\/2024.10.02.616147"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Howell, B.R., et al.: The UNC\/UMN baby connectome project (BCP): an overview of the study design and protocol development. NeuroImage, 185, 891\u2013905 (2019). ISSN 1053-8119. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.03.049","DOI":"10.1016\/j.neuroimage.2018.03.049"},{"key":"5_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.886772","volume":"16","author":"AD Edwards","year":"2022","unstructured":"Edwards, A.D., et al.: The developing human connectome project neonatal data release. Front. Neurosci. 16, 886772 (2022)","journal-title":"Front. Neurosci."},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Makropoulos, A., et al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage, 173, 88\u2013112 (2018). ISSN 1053-8119. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.01.054","DOI":"10.1016\/j.neuroimage.2018.01.054"},{"issue":"3","key":"5_CR22","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1002\/mrm.29273","volume":"88","author":"SCL Deoni","year":"2022","unstructured":"Deoni, S.C.L., O\u2019Muircheartaigh, J., Ljungberg, E., Huentelman, M., Williams, S.C.: Simultaneous high-resolution t2-weighted imaging and quantitative t 2 mapping at low magnetic field strengths using a multiple te and multi-orientation acquisition approach. Magnetic Res. Med. 88(3), 1273\u20131281 (2022)","journal-title":"Magnetic Res. Med."},{"key":"5_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106236","volume":"208","author":"F P\u00e9rez-Garc\u00eda","year":"2021","unstructured":"P\u00e9rez-Garc\u00eda, F., Sparks, R., Ourselin, S.: Torchio: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Methods Programs Biomed. 208, 106236 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"5_CR24","unstructured":"Eisenmann, M., et\u00a0al.: Why is the winner the best? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19955\u201319966 (2023)"},{"key":"5_CR25","unstructured":"MONAI Consortium. Monai: Medical open network for AI (2025)"}],"container-title":["Lecture Notes in Computer Science","Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-14417-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T03:12:15Z","timestamp":1776222735000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-14417-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032144164","9783032144171"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-14417-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"23 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"LISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lisa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.synapse.org\/Synapse:syn65670170\/wiki\/631438","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}