{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T09:20:16Z","timestamp":1768641616942,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076159"],"award-info":[{"award-number":["62076159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12031010"],"award-info":[{"award-number":["12031010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-023-00220-3","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T15:21:45Z","timestamp":1680621705000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["The nnU-Net based method for automatic segmenting fetal brain tissues"],"prefix":"10.1007","volume":"11","author":[{"given":"Ying","family":"Peng","sequence":"first","affiliation":[]},{"given":"Yandi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Mingzhao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Huiquan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6540-4397","authenticated-orcid":false,"given":"Juanying","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"issue":"4","key":"220_CR1","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s12687-018-0390-4","volume":"9","author":"B Modell","year":"2018","unstructured":"Modell B, Darlison MW, Malherbe H, Moorthie S, Blencowe H, Mahaini R, El-Adawy M. Congenital disorders: epidemiological methods for answering calls for action. J Commun Genetics. 2018;9(4):335\u201340.","journal-title":"J Commun Genetics"},{"key":"220_CR2","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.neuroimage.2017.06.074","volume":"170","author":"A Makropoulos","year":"2018","unstructured":"Makropoulos A, Counsell SJ, Rueckert D. A review on automatic fetal and neonatal brain MRI segmentation. NeuroImage. 2018;170:231\u201348.","journal-title":"NeuroImage"},{"key":"220_CR3","doi-asserted-by":"crossref","unstructured":"Habas PA, Kim K, Rousseau F, Glenn OA, Barkovich AJ, Studholme C. Atlas-based segmentation of the germinal matrix from in utero clinical MRI of the fetal brain. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 351\u2013358. Springer, 2008.","DOI":"10.1007\/978-3-540-85988-8_42"},{"issue":"2","key":"220_CR4","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2010.06.054","volume":"53","author":"PA Habas","year":"2010","unstructured":"Habas PA, Kim K, Corbett-Detig JM, Rousseau F, Glenn OA, Barkovich AJ, Studholme C. A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation. NeuroImage. 2010;53(2):460\u201370.","journal-title":"NeuroImage"},{"issue":"3","key":"220_CR5","first-page":"1","volume":"2012","author":"A Serag","year":"2012","unstructured":"Serag A, Kyriakopoulou V, Rutherford MA, Edwards AD, Hajnal JV, Aljabar P, Counsell SJ, Boardman J, Rueckert D. A multi-channel 4D probabilistic atlas of the developing brain: application to fetuses and neonates. Ann BMVA. 2012;2012(3):1\u201314.","journal-title":"Ann BMVA"},{"issue":"4","key":"220_CR6","volume":"9","author":"R Sarki","year":"2022","unstructured":"Sarki R, Ahmed K, Wang H, Zhang Y, Wang K. Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans Scalable Inf Syst. 2022;9(4): e15.","journal-title":"EAI Endorsed Trans Scalable Inf Syst"},{"issue":"32","key":"220_CR7","first-page":"1","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki R, Ahmed K, Wang H, Zhang Y. Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst. 2020;8(32):1\u20139.","journal-title":"Health Inf Sci Syst"},{"issue":"9","key":"220_CR8","first-page":"1","volume":"10","author":"D Pandey","year":"2022","unstructured":"Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf Sci Syst. 2022;10(9):1\u201319.","journal-title":"Health Inf Sci Syst"},{"issue":"21","key":"220_CR9","first-page":"1","volume":"7","author":"D Jiahua","year":"2019","unstructured":"Jiahua D, Michalska S, Subramani S, Wang H, Zhang Y. Neural attention with character embeddings for hay fever detection from Twitter. Health Inf Sci Syst. 2019;7(21):1\u20137.","journal-title":"Health Inf Sci Syst"},{"key":"220_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3186180","author":"AM Alvi","year":"2022","unstructured":"Alvi AM, Siuly S, Wang H. A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans Emerg Top Comput Intell. 2022. https:\/\/doi.org\/10.1109\/TETCI.2022.3186180.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"33","key":"220_CR11","first-page":"1","volume":"8","author":"S Supriya","year":"2020","unstructured":"Supriya S, Siuly S, Wang H, Zhang Y. Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf Sci Syst. 2020;8(33):1\u201315.","journal-title":"Health Inf Sci Syst"},{"issue":"5","key":"220_CR12","doi-asserted-by":"publisher","first-page":"2835","DOI":"10.1007\/s11280-019-00776-9","volume":"23","author":"J He","year":"2020","unstructured":"He J, Rong J, Sun L, Wang H, Zhang Y, Ma J. A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web. 2020;23(5):2835\u201350.","journal-title":"World Wide Web"},{"key":"220_CR13","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.mri.2019.05.020","volume":"64","author":"N Khalili","year":"2019","unstructured":"Khalili N, Lessmann N, Turk E, Claessens N, de Heus R, Kolk T, Viergever MA, Benders MJ, I\u0161gum I. Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn Reson Imaging. 2019;64:77\u201389.","journal-title":"Magn Reson Imaging"},{"key":"220_CR14","doi-asserted-by":"crossref","unstructured":"Payette K, Kottke R, Jakab A. Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels. In Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, pp. 295\u2013304. Springer, 2020.","DOI":"10.1007\/978-3-030-60334-2_29"},{"key":"220_CR15","doi-asserted-by":"publisher","first-page":"591683","DOI":"10.3389\/fnins.2020.591683","volume":"14","author":"J Hong","year":"2020","unstructured":"Hong J, Yun HJ, Park G, Kim S, Laurentys CT, Siqueira LC, Tarui T, Rollins CK, Ortinau CM, Grant PE, Lee JM. Fetal cortical plate segmentation using fully convolutional networks with multiple plane aggregation. Front Neurosci. 2020;14:591683.","journal-title":"Front Neurosci"},{"issue":"4","key":"220_CR16","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TMI.2020.3046579","volume":"40","author":"H Dou","year":"2021","unstructured":"Dou H, Karimi D, Rollins CK, Ortinau CM, Vasung L, Velasco-Annis C, Ouaalam A, Yang X, Ni D, Gholipour A. A deep attentive convolutional neural network for automatic cortical plate segmentation in fetal MRI. IEEE Trans Med Imaging. 2021;40(4):1123\u201333.","journal-title":"IEEE Trans Med Imaging"},{"key":"220_CR17","doi-asserted-by":"crossref","unstructured":"de Dumast P, Kebiri H, Atat C, Dunet V, Koob M, Cuadra MB. Segmentation of the cortical plate in fetal brain MRI with a topological loss. In: Uncertainty for safe utilization of machine learning in medical imaging, and perinatal imaging, placental and preterm image analysis, pp. 200\u2013209. Springer, 2021.","DOI":"10.1007\/978-3-030-87735-4_19"},{"issue":"12","key":"220_CR18","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481\u201395.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"220_CR19","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang J, Shen C, Xia Y. Cotr: Efficiently bridging CNN and transformer for 3D medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 171\u2013180. Springer, 2021.","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"220_CR20","doi-asserted-by":"crossref","unstructured":"Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y. Learning active contour models for medical image segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11632\u201311640, 2019.","DOI":"10.1109\/CVPR.2019.01190"},{"issue":"5","key":"220_CR21","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett. 2018;15(5):749\u201353.","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"2","key":"220_CR22","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 PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203\u201311.","journal-title":"Nat Methods"},{"issue":"1","key":"220_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-021-00946-3","volume":"8","author":"K Payette","year":"2021","unstructured":"Payette K, de Dumast P, Kebiri H, Ezhov I, Paetzold JC, Shit S, Iqbal A, Khan R, Kottke R, Grehten P, Ji H. An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sci Data. 2021;8(1):1\u201314.","journal-title":"Sci Data"},{"key":"220_CR24","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.neuroimage.2015.06.018","volume":"118","author":"S Tourbier","year":"2015","unstructured":"Tourbier S, Bresson X, Hagmann P, Thiran JP, Meuli R, Cuadra MB. An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization. NeuroImage. 2015;118:584\u201397.","journal-title":"NeuroImage"},{"issue":"8","key":"220_CR25","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1016\/j.media.2012.07.004","volume":"16","author":"M Kuklisova-Murgasova","year":"2012","unstructured":"Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal. 2012;16(8):1550\u201364.","journal-title":"Med Image Anal"},{"key":"220_CR26","doi-asserted-by":"crossref","unstructured":"Payette K, Li H, de Dumast P, Licandro R, Ji H, Siddiquee MM, Xu D, Myronenko A, Liu H, Pei Y, Wang L. Fetal brain tissue annotation and segmentation challenge results. 2022. arXiv:2204.09573","DOI":"10.1016\/j.media.2023.102833"},{"issue":"1","key":"220_CR27","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk T, Mai D, Bensch R, \u00c7i\u00e7ek \u00d6, Abdulkadir A, Marrakchi Y, B\u00f6hm A, Deubner J, J\u00e4ckel Z, Seiwald K, et al. U-net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1):67\u201370.","journal-title":"Nat Methods"},{"issue":"3","key":"220_CR28","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297\u2013302.","journal-title":"Ecology"},{"issue":"9","key":"220_CR29","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993;15(9):850\u201363.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"220_CR30","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834\u201348.","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-023-00220-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-023-00220-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-023-00220-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T10:15:23Z","timestamp":1702635323000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-023-00220-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,27]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["220"],"URL":"https:\/\/doi.org\/10.1007\/s13755-023-00220-3","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,27]]},"assertion":[{"value":"24 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is not any conflict of interest in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"17"}}