{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:48:06Z","timestamp":1775022486748,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732591","type":"print"},{"value":"9783031732607","type":"electronic"}],"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_7","type":"book-chapter","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:01:54Z","timestamp":1728450114000},"page":"70-81","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Accurate Fetal Brain Parcellation via\u00a0Hierarchical Network and\u00a0Loss"],"prefix":"10.1007","author":[{"given":"Shijie","family":"Huang","sequence":"first","affiliation":[]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiawei","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Lingnan","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Fangmei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zhongxiang","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Geng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"issue":"365","key":"7_CR1","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ants). Insight J. 2(365), 1\u201335 (2009)","journal-title":"Insight J."},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"102789","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"B Billot","year":"2021","unstructured":"Billot, B., et al.: Synthseg: segmentation of brain MRI scans of any contrast and resolution without retraining. Med. Image Anal. 86, 102789\u2013102789 (2021)","journal-title":"Med. Image Anal."},{"key":"7_CR3","unstructured":"Cardoso, M.J., et al.: Monai: an open-source framework for deep learning in healthcare. ArXiv arxiv:2211.02701 (2022)"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Dey, N., et al.: Anystar: domain randomized universal star-convex 3d instance segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 7593\u20137603 (2024)","DOI":"10.1109\/WACV57701.2024.00742"},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TMI.2020.3046579","volume":"40","author":"H Dou","year":"2020","unstructured":"Dou, H., et al.: A deep attentive convolutional neural network for automatic cortical plate segmentation in fetal MRI. IEEE Trans. Med. Imaging 40, 1123\u20131133 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou, Q., et al.: 3d deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40\u201354 (2017)","journal-title":"Med. Image Anal."},{"key":"7_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-030-87735-4_19","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis","author":"P de Dumast","year":"2021","unstructured":"de Dumast, P., Kebiri, H., Atat, C., Dunet, V., Koob, M., Cuadra, M.B.: Segmentation of the cortical plate in\u00a0fetal brain MRI with a topological loss. In: Sudre, C.H., et al. (eds.) UNSURE\/PIPPI -2021. LNCS, vol. 12959, pp. 200\u2013209. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87735-4_19"},{"key":"7_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/978-3-030-87196-3_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"L Fidon","year":"2021","unstructured":"Fidon, L., et al.: Label-set loss functions for partial supervision: application to fetal brain 3D MRI parcellation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 647\u2013657. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_60"},{"key":"7_CR9","unstructured":"Fidon, L., et al.: Distributionally robust segmentation of abnormal fetal brain 3d mri. ArXiv arxiv:2108.04175 (2021)"},{"key":"7_CR10","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1038\/s41598-017-00525-w"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Yang, D., Roth, H.R., Xu, D.: Unetr: transformers for 3d medical image segmentation. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1748\u20131758 (2021)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"7_CR12","unstructured":"Huang, G., Chen, D., Li, T., Wu, F., van\u00a0der Maaten, L., Weinberger, K.Q.: Multi-scale dense networks for resource efficient image classification. In: International Conference on Learning Representations (2017)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Iglesias, J.E., et al.: Synthsr: a public AI tool to turn heterogeneous clinical brain scans into high-resolution t1-weighted images for 3d morphometry. Sci. Adv. 9(5), eadd3607 (2023)","DOI":"10.1126\/sciadv.add3607"},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2020","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2020)","journal-title":"Nat. Methods"},{"key":"7_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102731","volume":"85","author":"D Karimi","year":"2022","unstructured":"Karimi, D., Rollins, C.K., Velasco-Annis, C., Ouaalam, A., Gholipour, A.: Learning to segment fetal brain tissue from noisy annotations. Med. Image Anal. 85, 102731 (2022)","journal-title":"Med. Image Anal."},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-031-23223-7_11","volume-title":"EPIMI\/ML-CDS@MICCAI","author":"L Li","year":"2022","unstructured":"Li, L., et al.: Fetal cortex segmentation with topology and thickness loss constraints. In: Baxter, J.S.H., et al. (eds.) EPIMI\/ML-CDS@MICCAI, pp. 123\u2013133. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-23223-7_11"},{"key":"7_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-3-030-87735-4_21","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis","author":"L Li","year":"2021","unstructured":"Li, L., et al.: CAS-net: conditional atlas generation and brain segmentation for fetal MRI. In: Sudre, C.H., et al. (eds.) UNSURE\/PIPPI -2021. LNCS, vol. 12959, pp. 221\u2013230. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87735-4_21"},{"key":"7_CR19","unstructured":"Machado-Rivas, F., et al.: Normal growth, sexual dimorphism, and lateral asymmetries at fetal brain MRI. Radiology, 211222 (2021)"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1109\/TMI.2014.2322280","volume":"33","author":"A Makropoulos","year":"2014","unstructured":"Makropoulos, A., et al.: Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 33, 1818\u20131831 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Payette, K., et al.: An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sci. Data 8 (2020)","DOI":"10.1038\/s41597-021-00946-3"},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Pei, Y., et al.: Learning spatiotemporal probabilistic atlas of fetal brains with anatomically constrained registration network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 239\u2013248. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_23","DOI":"10.1007\/978-3-030-87234-2_23"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Rutherford, M., et al.: Mr imaging methods for assessing fetal brain development. Dev. Neurobiol. 68 (2008)","DOI":"10.1002\/dneu.20614"},{"key":"7_CR24","unstructured":"Sm, C.K.R.M., et al.: Regional brain growth trajectories in fetuses with congenital heart disease. Ann. Neurol. 89 (2020)"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Vasung, L., et al.: Abnormal development of transient fetal zones in mild isolated fetal ventriculomegaly. Cerebral cortex (2022)","DOI":"10.1093\/cercor\/bhac125"},{"key":"7_CR26","doi-asserted-by":"publisher","first-page":"9435","DOI":"10.1523\/JNEUROSCI.1285-22.2022","volume":"42","author":"X Xu","year":"2022","unstructured":"Xu, X., et al.: Spatiotemporal atlas of the fetal brain depicts cortical developmental gradient. J. Neurosci. 42, 9435\u20139449 (2022)","journal-title":"J. Neurosci."},{"key":"7_CR27","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.R., 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"}],"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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:02:34Z","timestamp":1728450154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73260-7_7"}},"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_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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"}}]}}