{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:31:57Z","timestamp":1787041917416,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059963","type":"print"},{"value":"9783032059970","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05997-0_5","type":"book-chapter","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T07:23:53Z","timestamp":1758525833000},"page":"48-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization"],"prefix":"10.1007","author":[{"given":"Marina","family":"Grifell I Plana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladyslav","family":"Zalevskyi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u00e9a","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yvan","family":"Gomez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Dunet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e9riam","family":"Koob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Siffredi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meritxell Bach","family":"Cuadra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"4","key":"5_CR1","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1046\/j.0960-7692.2001.00512.x","volume":"18","author":"R Achiron","year":"2001","unstructured":"Achiron, R., et al.: Development of the human fetal corpus callosum: a high-resolution, cross-sectional sonographic study. Ultrasound Obstet. Gynecol. 18(4), 343\u2013347 (2001)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Mahallati, H., et\u00a0al.: Heterogeneity in defining fetal corpus callosal pathology: systematic review. Ultrasound Obstet. Gynecol. 58 (2021)","DOI":"10.1002\/uog.22179"},{"key":"5_CR3","unstructured":"Moradi, B., et\u00a0al.: Fetal corpus callosum abnormalities: ultrasound and magnetic resonance imaging role. J. Clin. Ultrasound, 1\u201315 (2022)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Miller, S., et\u00a0al.: The consequences of fetal growth restriction on brain structure and neurodevelopmental outcome. J. Physiol. 594(4) (2016)","DOI":"10.1113\/JP271402"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Gaudfernau, F., et\u00a0al.: Analysis of the anatomical variability of fetal brains with corpus callosum agenesis. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis, pp. 274\u2013283, Springer (2021)","DOI":"10.1007\/978-3-030-87735-4_26"},{"key":"5_CR6","unstructured":"Price, A., et\u00a0al.: The developing human connectome project (dHCP): fetal acquisition protocol. In: Proceedings of the annual meeting of the International Society of Magnetic Resonance in Medicine (ISMRM), vol.\u00a0244, International Society for Magnetic Resonance in Medicine (ISMRM) (2019)"},{"key":"5_CR7","unstructured":"Payette, K., Jakab, A.: Fetal Tissue Annotation Dataset (FeTA) (2021). https:\/\/zenodo.org\/record\/4541605"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Fidon, L., et\u00a0al.: Distributionally robust segmentation of abnormal fetal brain 3d MRI. In: UNSURE and PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 3, pp.\u00a0263\u2013273, Springer (2021)","DOI":"10.1007\/978-3-030-87735-4_25"},{"key":"5_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2024.120603","volume":"292","author":"T Ciceri","year":"2024","unstructured":"Ciceri, T., et al.: Fetal brain MRI atlases and datasets: a review. Neuroimage 292, 120603 (2024)","journal-title":"Neuroimage"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, X., et\u00a0al.: CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation, pp.\u00a0196\u2013205. Springer International Publishing (2021)","DOI":"10.1007\/978-3-030-87193-2_19"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Liu, P., et\u00a0al.: PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI, pp.\u00a0676\u2013686. Springer Nature Switzerland (2024)","DOI":"10.1007\/978-3-031-72390-2_63"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Basaran, B., et\u00a0al.: Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images, pp.\u00a01\u201311. Springer International Publishing (2022)","DOI":"10.1007\/978-3-031-16980-9_1"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1038\/s42256-024-00864-0","volume":"6","author":"P Tudosiu","year":"2024","unstructured":"Tudosiu, P., et al.: Realistic morphology-preserving generative modelling of the brain. Nat. Mach. Intell. 6, 811\u2013819 (2024)","journal-title":"Nat. Mach. Intell."},{"key":"5_CR14","unstructured":"Aguila, A., et\u00a0al.: Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization (2025)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Liu, P., et\u00a0al.: Unraveling normal anatomy via fluid-driven anomaly randomization. (2025)","DOI":"10.1109\/CVPR52734.2025.00978"},{"key":"5_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"B Benjamin","year":"2023","unstructured":"Benjamin, 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."},{"key":"5_CR17","unstructured":"Zalevskyi, V., et\u00a0al.: Drifts: optimizing domain randomization with synthetic data and weight interpolation for fetal brain tissue segmentation. (2024)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Shang, Z., et\u00a0al.: Towards contrast-and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg. arXiv preprint arXiv:2504.10244 (2025)","DOI":"10.1016\/j.neuroimage.2026.121729"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Ebner, M., et\u00a0al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage 206 (2020)","DOI":"10.1016\/j.neuroimage.2019.116324"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Uus, A., et\u00a0al.: BOUNTI: brain volumetry and automated parcellation for 3d fetal MRI bioRxiv, (2023). Preprint","DOI":"10.7554\/eLife.88818"},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/TMI.2023.3236216","volume":"42","author":"J Xu","year":"2023","unstructured":"Xu, J., et al.: NeSVoR: implicit neural representation for slice-to-volume reconstruction in MRI. IEEE Trans. Med. Imaging 42, 1707\u20131719 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR22","unstructured":"A, Y.P., et\u00a0al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage (2006)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Gousias, I., et\u00a0al.: Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants. NeuroImage (2012)","DOI":"10.1016\/j.neuroimage.2012.05.083"},{"key":"5_CR24","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/s41597-021-00946-3","volume":"8","author":"K Payette","year":"2021","unstructured":"Payette, K., et al.: An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sci. Data 8, 167 (2021)","journal-title":"Sci. Data"},{"key":"5_CR25","unstructured":"Tourbier, S., et\u00a0al.: Medical-image-analysis-laboratory\/mialsuperresolutiontoolkit: MIAL super-resolution toolkit v2.0.1 (2020)"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"M, K.M., et\u00a0al.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16(8) (2012)","DOI":"10.1016\/j.media.2012.07.004"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Fidon, L., et\u00a0al.: Label-set loss functions for partial supervision: application to fetal brain 3D MRI parcellation. Zenodo (2021)","DOI":"10.1007\/978-3-030-87196-3_60"},{"key":"5_CR28","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1002\/mrm.26462","volume":"78","author":"EJ Hughes","year":"2017","unstructured":"Hughes, E.J., et al.: A dedicated neonatal brain imaging system. Magn. Reson. Med. 78, 794\u2013804 (2017). Epub 2016 Sep 19","journal-title":"Magn. Reson. Med."},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"J. S., et\u00a0al.: MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies. IEEE Trans. Med. Imaging 26(7), 967\u2013980 (2007)","DOI":"10.1109\/TMI.2007.895456"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Makropoulos, A., et\u00a0al.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage (2018)","DOI":"10.1016\/j.neuroimage.2018.01.054"},{"key":"5_CR31","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1038\/s41598-017-00525-w","volume":"7","author":"A Gholipour","year":"2017","unstructured":"Gholipour, A., et al.: A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci. Rep. 7, 476 (2017)","journal-title":"Sci. Rep."},{"issue":"10","key":"5_CR32","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1109\/TMI.2010.2051680","volume":"29","author":"A Gholipour","year":"2010","unstructured":"Gholipour, A., et al.: Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans. Med. Imaging 29(10), 1739\u20131758 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR33","unstructured":"Zalevskyi, V.: 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)"},{"issue":"4","key":"5_CR34","doi-asserted-by":"publisher","first-page":"352","DOI":"10.4103\/JPN.JPN_174_19","volume":"15","author":"C Manor","year":"2020","unstructured":"Manor, C., et al.: Magnetic resonance imaging findings in fetal corpus callosal developmental abnormalities: a pictorial essay. J. Pediatr. Neurosci. 15(4), 352\u2013357 (2020)","journal-title":"J. Pediatr. Neurosci."},{"key":"5_CR35","unstructured":"Consortium, M.: Monai: medical open network for AI (2025)"},{"key":"5_CR36","doi-asserted-by":"publisher","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. In: Cardoso, M.J., Arbel, T., Carneiro, G., Syeda-Mahmood, T., Tavares, J.M.R.S., Moradi, M., Bradley, A., Greenspan, H., Papa, J.P., Madabhushi, A., Nascimento, J.C., Cardoso, J.S., Belagiannis, V., Lu, Z. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Huttenlocher, D., et al.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9) (1993)","DOI":"10.1109\/34.232073"},{"issue":"1","key":"5_CR38","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"A Taha","year":"2015","unstructured":"Taha, A., et al.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015)","journal-title":"BMC Med. Imaging"},{"key":"5_CR39","unstructured":"Payette, K.,, et al.: Multi-center fetal brain tissue annotation (feta) challenge 2022 results. IEEE Trans. Med. Imaging 44(3) (2025)"},{"key":"5_CR40","doi-asserted-by":"publisher","first-page":"1358741","DOI":"10.3389\/fneur.2024.1358741","volume":"15","author":"S Lamon","year":"2024","unstructured":"Lamon, S., et al.: Assessment of fetal corpus callosum biometry by 3D super-resolution reconstructed t2-weighted magnetic resonance imaging. Front. Neurol. 15, 1358741 (2024)","journal-title":"Front. Neurol."},{"key":"5_CR41","unstructured":"Zalevskyi, V., et\u00a0al.: Advances in automated fetal brain MRI segmentation and biometry: insights from the feta 2024 challenge (2025)"}],"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\/978-3-032-05997-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:23:13Z","timestamp":1787041393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05997-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032059963","9783032059970"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05997-0_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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":"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":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pippi2025","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"}}]}}