{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:51:01Z","timestamp":1770753061569,"version":"3.50.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH110793"],"award-info":[{"award-number":["R01MH110793"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH122447"],"award-info":[{"award-number":["R01MH122447"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["R01ES032294"],"award-info":[{"award-number":["R01ES032294"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["R34DA050287"],"award-info":[{"award-number":["R34DA050287"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.<\/jats:p>","DOI":"10.1007\/s12021-021-09528-5","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T11:02:39Z","timestamp":1623754959000},"page":"173-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Automated Brain Masking of Fetal Functional MRI with Open Data"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3006-9044","authenticated-orcid":false,"given":"Saige","family":"Rutherford","sequence":"first","affiliation":[]},{"given":"Pascal","family":"Sturmfels","sequence":"additional","affiliation":[]},{"given":"Mike","family":"Angstadt","sequence":"additional","affiliation":[]},{"given":"Jasmine","family":"Hect","sequence":"additional","affiliation":[]},{"given":"Jenna","family":"Wiens","sequence":"additional","affiliation":[]},{"given":"Marion I.","family":"van den Heuvel","sequence":"additional","affiliation":[]},{"given":"Dustin","family":"Scheinost","sequence":"additional","affiliation":[]},{"given":"Chandra","family":"Sripada","sequence":"additional","affiliation":[]},{"given":"Moriah","family":"Thomason","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"9528_CR1","doi-asserted-by":"publisher","unstructured":"Anderson, A. L., & Thomason, M. E. (2013). Functional plasticity before the cradle: A review of neural functional imaging in the human fetus. Neuroscience & Biobehavioral Reviews, 37(9, Part B), 2220\u20132232. https:\/\/doi.org\/10.1016\/j.neubiorev.2013.03.013.","DOI":"10.1016\/j.neubiorev.2013.03.013"},{"key":"9528_CR2","doi-asserted-by":"publisher","unstructured":"Benkarim, O. M., Sanroma, G., Zimmer, V. A., Mu\u00f1oz-Moreno, E., Hahner, N., Eixarch, E., Camara, O., Ballester, M. A. G., & Piella, G. (n.d.). Toward the automatic quantification of in utero brain development in 3D structural MRI: A review. Human Brain Mapping, 38(5), 2772\u20132787. https:\/\/doi.org\/10.1002\/hbm.23536.","DOI":"10.1002\/hbm.23536"},{"issue":"8","key":"9528_CR3","doi-asserted-by":"publisher","first-page":"523","DOI":"10.4329\/wjr.v6.i8.523","volume":"6","author":"A Biegon","year":"2014","unstructured":"Biegon, A., & Hoffmann, C. (2014). Quantitative magnetic resonance imaging of the fetal brain in utero: Methods and applications. World Journal of Radiology, 6(8), 523\u2013529. https:\/\/doi.org\/10.4329\/wjr.v6.i8.523.","journal-title":"World Journal of Radiology"},{"key":"9528_CR4","doi-asserted-by":"publisher","unstructured":"Bozek, J., Makropoulos, A., Schuh, A., Fitzgibbon, S., Wright, R., Glasser, M. F., Coalson, T. S., O\u2019Muircheartaigh, J., Hutter, J., Price, A. N., Cordero-Grande, L., Teixeira, R. P. A. G., Hughes, E., Tusor, N., Baruteau, K. P., Rutherford, M. A., Edwards, A. D., Hajnal, J. V., Smith, S. M., \u2026 Robinson, E. C. (2018). Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project. NeuroImage, 179, 11\u201329. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.06.018.","DOI":"10.1016\/j.neuroimage.2018.06.018"},{"issue":"6","key":"9528_CR5","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1001\/jama.2017.7797","volume":"318","author":"F Cabitza","year":"2017","unstructured":"Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517\u2013518. https:\/\/doi.org\/10.1001\/jama.2017.7797.","journal-title":"JAMA"},{"key":"9528_CR6","unstructured":"Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. (2015). How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? ArXiv:1511.06348 [Cs]. http:\/\/arxiv.org\/abs\/1511.06348."},{"issue":"3","key":"9528_CR7","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1006\/cbmr.1996.0014","volume":"29","author":"RW Cox","year":"1996","unstructured":"Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162\u2013173.","journal-title":"Computers and Biomedical Research"},{"issue":"11","key":"9528_CR8","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TMI.2006.880587","volume":"25","author":"WR Crum","year":"2006","unstructured":"Crum, W. R., Camara, O., & Hill, D. L. G. (2006). Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Transactions on Medical Imaging, 25(11), 1451\u20131461. https:\/\/doi.org\/10.1109\/TMI.2006.880587.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"9528_CR9","doi-asserted-by":"publisher","first-page":"116324","DOI":"10.1016\/j.neuroimage.2019.116324","volume":"206","author":"M Ebner","year":"2020","unstructured":"Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., Melbourne, A., Doel, T., Dymarkowski, S., De Coppi, P., David, A. L., Deprest, J., Ourselin, S., & Vercauteren, T. (2020). An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage, 206, 116324. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.116324.","journal-title":"NeuroImage"},{"issue":"10","key":"9528_CR10","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1016\/S0031-3203(01)00178-9","volume":"35","author":"M Egmont-Petersen","year":"2002","unstructured":"Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks\u2014A review. Pattern Recognition, 35(10), 2279\u20132301.","journal-title":"Pattern Recognition"},{"key":"9528_CR11","doi-asserted-by":"publisher","unstructured":"Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2018). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 1. https:\/\/doi.org\/10.1038\/s41592-018-0235-4.","DOI":"10.1038\/s41592-018-0235-4"},{"key":"9528_CR12","doi-asserted-by":"publisher","unstructured":"Falk, T., Mai, D., Bensch, R., \u00c7i\u00e7ek, \u00d6, Abdulkadir, A., Marrakchi, Y., B\u00f6hm, A., Deubner, J., J\u00e4ckel, Z., Seiwald, K., Dovzhenko, A., Tietz, O., Bosco, C. D., Walsh, S., Saltukoglu, D., Tay, T. L., Prinz, M., Palme, K., Simons, M., \u2026 Ronneberger, O. (2018). U-Net: Deep learning for cell counting, detection, and morphometry. Nature Methods, 1. https:\/\/doi.org\/10.1038\/s41592-018-0261-2.","DOI":"10.1038\/s41592-018-0261-2"},{"key":"9528_CR13","doi-asserted-by":"publisher","unstructured":"Fitzgibbon, S. P., Harrison, S. J., Jenkinson, M., Baxter, L., Robinson, E. C., Bastiani, M., Bozek, J., Karolis, V., Grande, C., Price, L., Hughes, A. N., Makropoulos, E., Passerat-Palmbach, A., Schuh, J., Gao, A., Farahibozorg, J., O\u2019Muircheartaigh, S.-R., Ciarrusta, J., O\u2019Keeffe, J. C., \u2026 Andersson, J (2020). The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. NeuroImage, 223, 117303. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117303.","DOI":"10.1016\/j.neuroimage.2020.117303"},{"issue":"2","key":"9528_CR14","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/TMI.2013.2284014","volume":"33","author":"M Fogtmann","year":"2014","unstructured":"Fogtmann, M., Seshamani, S., Kroenke, C., Cheng, X., Chapman, T., Wilm, J., Rousseau, F., & Studholme, C. (2014). A unified approach to diffusion direction sensitive slice registration and 3-D DTI reconstruction from moving fetal brain anatomy. IEEE Transactions on Medical Imaging, 33(2), 272\u2013289. https:\/\/doi.org\/10.1109\/TMI.2013.2284014.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"9528_CR15","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1038\/s41598-017-00525-w","volume":"7","author":"A Gholipour","year":"2017","unstructured":"Gholipour, A., Rollins, C. K., Velasco-Annis, C., Ouaalam, A., Akhondi-Asl, A., Afacan, O., Ortinau, C. M., Clancy, S., Limperopoulos, C., Yang, E., Estroff, J. A., & Warfield, S. K. (2017). A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Scientific Reports, 7(1), 476. https:\/\/doi.org\/10.1038\/s41598-017-00525-w.","journal-title":"Scientific Reports"},{"key":"9528_CR16","doi-asserted-by":"publisher","unstructured":"Harms, M. P., Somerville, L. H., Ances, B. M., Andersson, J., Barch, D. M., Bastiani, M., Bookheimer, S. Y., Brown, T. B., Buckner, R. L., Burgess, G. C., Coalson, T. S., Chappell, M. A., Dapretto, M., Douaud, G., Fischl, B., Glasser, M. F., Greve, D. N., Hodge, C., Jamison, K. W., \u2026 Yacoub, E. (2018). Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. NeuroImage, 183, 972\u2013984. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.09.060.","DOI":"10.1016\/j.neuroimage.2018.09.060"},{"key":"9528_CR17","doi-asserted-by":"publisher","unstructured":"Huang, W., Bolton, T. A. W., Medaglia, J. D., Bassett, D. S., Ribeiro, A., & Ville, D. V. D. (2018). A graph signal processing perspective on functional brain imaging. Proceedings of the IEEE, PP(99), 1\u201318. https:\/\/doi.org\/10.1109\/JPROC.2018.2798928.","DOI":"10.1109\/JPROC.2018.2798928"},{"key":"9528_CR18","first-page":"17","volume":"MICCAI","author":"M Ison","year":"2012","unstructured":"Ison, M., Donner, R., Dittrich, E., Kasprian, G., Prayer, D., & Langs, G. (2012). Fully automated brain extraction and orientation in raw fetal MRI. Workshop on Paediatric and Perinatal Imaging, MICCAI, 17\u201324.","journal-title":"Workshop on Paediatric and Perinatal Imaging"},{"key":"9528_CR19","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.neuroimage.2015.02.038","volume":"111","author":"A Jakab","year":"2015","unstructured":"Jakab, A., Kasprian, G., Schwartz, E., Gruber, G. M., Mitter, C., Prayer, D., Sch\u00f6pf, V., & Langs, G. (2015). Disrupted developmental organization of the structural connectome in fetuses with corpus callosum agenesis. NeuroImage, 111, 277\u2013288. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.02.038.","journal-title":"NeuroImage"},{"key":"9528_CR20","doi-asserted-by":"publisher","first-page":"852","DOI":"10.3389\/fnhum.2014.00852","volume":"8","author":"A Jakab","year":"2014","unstructured":"Jakab, A., Schwartz, E., Kasprian, G., Gruber, G. M., Prayer, D., Sch\u00f6pf, V., & Langs, G. (2014). Fetal functional imaging portrays heterogeneous development of emerging human brain networks. Frontiers in Human Neuroscience, 8, 852. https:\/\/doi.org\/10.3389\/fnhum.2014.00852.","journal-title":"Frontiers in Human Neuroscience"},{"issue":"2","key":"9528_CR21","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825\u2013841.","journal-title":"NeuroImage"},{"key":"9528_CR22","unstructured":"Karimi, D., & Salcudean, S. E. (2019). Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. ArXiv:1904.10030 [Cs, Eess, Stat]. http:\/\/arxiv.org\/abs\/1904.10030."},{"key":"9528_CR23","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, M. A., Benders, M. J. N. L., & I\u0161gum, I. (2019). Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magnetic Resonance Imaging, 64, 77\u201389. https:\/\/doi.org\/10.1016\/j.mri.2019.05.020.","journal-title":"Magnetic Resonance Imaging"},{"key":"9528_CR24","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: a method for stochastic optimization. ArXiv:1412.6980 [Cs]. http:\/\/arxiv.org\/abs\/1412.6980."},{"key":"9528_CR25","doi-asserted-by":"publisher","first-page":"94130Q","DOI":"10.1117\/12.2081139","volume":"9413","author":"T Klinder","year":"2015","unstructured":"Klinder, T., Wendland, H., Wachter-Stehle, I., Roundhill, D., & Lorenz, C. (2015). Adaptation of an articulated fetal skeleton model to three-dimensional fetal image data. Medical Imaging 2015: Image Processing, 9413, 94130Q. https:\/\/doi.org\/10.1117\/12.2081139.","journal-title":"Medical Imaging 2015: Image Processing"},{"issue":"4","key":"9528_CR26","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.1016\/j.neuroimage.2010.10.019","volume":"54","author":"M Kuklisova-Murgasova","year":"2011","unstructured":"Kuklisova-Murgasova, M., Aljabar, P., Srinivasan, L., Counsell, S. J., Doria, V., Serag, A., Gousias, I. S., Boardman, J. P., Rutherford, M. A., Edwards, A. D., Hajnal, J. V., & Rueckert, D. (2011). A dynamic 4D probabilistic atlas of the developing brain. NeuroImage, 54(4), 2750\u20132763. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.10.019.","journal-title":"NeuroImage"},{"key":"9528_CR27","doi-asserted-by":"publisher","unstructured":"Kurtzer, G. M. (2016). Singularity 2.1.2\u2014Linux application and environment containers for science. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.60736.","DOI":"10.5281\/zenodo.60736"},{"key":"9528_CR28","doi-asserted-by":"publisher","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791.","DOI":"10.1109\/5.726791"},{"key":"9528_CR29","doi-asserted-by":"publisher","DOI":"10.1159\/000475548","author":"D Link","year":"2017","unstructured":"Link, D., Braginsky, M. B., Joskowicz, L., Ben Sira, L., Harel, S., Many, A., Tarrasch, R., Malinger, G., Artzi, M., Kapoor, C., Miller, E., & Ben Bashat, D. (2017). Automatic measurement of fetal brain development from magnetic resonance imaging: new reference data. Fetal Diagnosis and Therapy. https:\/\/doi.org\/10.1159\/000475548.","journal-title":"Fetal Diagnosis and Therapy"},{"key":"9528_CR30","doi-asserted-by":"publisher","unstructured":"Makropoulos, A., Robinson, E. C., Schuh, A., Wright, R., Fitzgibbon, S., Bozek, J., Counsell, S. J., Steinweg, J., Vecchiato, K., Passerat-Palmbach, J., Lenz, G., Mortari, F., Tenev, T., Duff, E. P., Bastiani, M., Cordero-Grande, L., Hughes, E., Tusor, N., Tournier, J.-D., \u2026 Rueckert, D. (n.d.). The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.01.054.","DOI":"10.1016\/j.neuroimage.2018.01.054"},{"key":"9528_CR31","unstructured":"Merkel, D. (2014). Docker: Lightweight Linux containers for consistent development and deployment. Linux Journal, 2014(239), 2:2."},{"issue":"7495","key":"9528_CR32","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/nature13185","volume":"508","author":"JA Miller","year":"2014","unstructured":"Miller, J. A., Ding, S.-L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., Arnold, J. M., Bennet, C., Bertagnolli, D., Brouner, K., Butler, S., Caldejon, S., Carey, A., Cuhaciyan, C., Dalley, R. A., \u2026 Lein, E. S. (2014). Transcriptional landscape of the prenatal human brain. Nature, 508(7495), 199\u2013206. https:\/\/doi.org\/10.1038\/nature13185.","journal-title":"Nature"},{"issue":"1","key":"9528_CR33","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1002\/mrm.26998","volume":"80","author":"RG Nunes","year":"2018","unstructured":"Nunes, R. G., Ferrazzi, G., Price, A. N., Hutter, J., Gaspar, A. S., Rutherford, M. A., & Hajnal, J. V. (2018). Inner-volume echo volumar imaging (IVEVI) for robust fetal brain imaging. Magnetic Resonance in Medicine, 80(1), 279\u2013285. https:\/\/doi.org\/10.1002\/mrm.26998.","journal-title":"Magnetic Resonance in Medicine"},{"key":"9528_CR34","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.ymeth.2014.10.025","volume":"0","author":"A Ouyang","year":"2015","unstructured":"Ouyang, A., Jeon, T., Sunkin, S. M., Pletikos, M., Sedmak, G., Sestan, N., Lein, E. S., & Huang, H. (2015). Spatial mapping of structural and connectional imaging data for the developing human brain with diffusion tensor imaging. Methods, 0, 27\u201337. https:\/\/doi.org\/10.1016\/j.ymeth.2014.10.025.","journal-title":"Methods"},{"key":"9528_CR35","doi-asserted-by":"publisher","unstructured":"Ouyang, M., Dubois, J., Yu, Q., Mukherjee, P., & Huang, H. (2018). Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. NeuroImage. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.04.017.","DOI":"10.1016\/j.neuroimage.2018.04.017"},{"key":"9528_CR36","doi-asserted-by":"crossref","unstructured":"Payette, K., de Dumast, P., Kebiri, H., Ezhov, I., Paetzold, J. C., Shit, S., Iqbal, A., Khan, R., Kottke, R., Grehten, P., Ji, H., Lanczi, L., Nagy, M., Beresova, M., Nguyen, T. D., Natalucci, G., Karayannis, T., Menze, B., Cuadra, M. B., & Jakab, A. (2021). An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. ArXiv:2010.15526 [Cs, Eess]. http:\/\/arxiv.org\/abs\/2010.15526.","DOI":"10.1038\/s41597-021-00946-3"},{"issue":"1","key":"9528_CR37","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1146\/annurev-psych-010814-015340","volume":"66","author":"A Qiu","year":"2015","unstructured":"Qiu, A., Mori, S., & Miller, M. I. (2015). Diffusion tensor imaging for understanding brain development in early life. Annual Review of Psychology, 66(1), 853\u2013876. https:\/\/doi.org\/10.1146\/annurev-psych-010814-015340.","journal-title":"Annual Review of Psychology"},{"key":"9528_CR38","unstructured":"Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., & Rueckert, D. (2016). DeepCut: object segmentation from bounding box annotations using convolutional neural networks. ArXiv:1605.07866 [Cs]. http:\/\/arxiv.org\/abs\/1605.07866."},{"issue":"4","key":"9528_CR39","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s00247-017-3965-z","volume":"48","author":"AJ Robinson","year":"2018","unstructured":"Robinson, A. J., & Ederies, M. A. (2018). Fetal neuroimaging: An update on technical advances and clinical findings. Pediatric Radiology, 48(4), 471\u2013485. https:\/\/doi.org\/10.1007\/s00247-017-3965-z.","journal-title":"Pediatric Radiology"},{"key":"9528_CR40","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.neuroimage.2017.10.037","volume":"167","author":"EC Robinson","year":"2018","unstructured":"Robinson, E. C., Garcia, K., Glasser, M. F., Chen, Z., Coalson, T. S., Makropoulos, A., Bozek, J., Wright, R., Schuh, A., Webster, M., Hutter, J., Price, A., Cordero Grande, L., Hughes, E., Tusor, N., Bayly, P. V., Van Essen, D. C., Smith, S. M., Edwards, A. D., \u2026 Rueckert, D. (2018). Multimodal surface matching with higher-order smoothness constraints. NeuroImage, 167, 453\u2013465. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.10.037.","journal-title":"NeuroImage"},{"key":"9528_CR41","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.neuroimage.2014.05.069","volume":"100","author":"EC Robinson","year":"2014","unstructured":"Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., Smith, S. M., Van Essen, D. C., & Jenkinson, M. (2014). MSM: A new flexible framework for Multimodal Surface Matching. NeuroImage, 100, 414\u2013426. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.05.069.","journal-title":"NeuroImage"},{"key":"9528_CR42","doi-asserted-by":"publisher","unstructured":"Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017). Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, PP(99), 1\u20131. https:\/\/doi.org\/10.1109\/TMI.2017.2721362.","DOI":"10.1109\/TMI.2017.2721362"},{"key":"9528_CR43","doi-asserted-by":"publisher","unstructured":"Salehi, S. S. M., Hashemi, S. R., Velasco-Annis, C., Ouaalam, A., Estroff, J. A., Erdogmus, D., Warfield, S. K., & Gholipour, A. (2018). Real-time automatic fetal brain extraction in fetal MRI by deep learning. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 720\u2013724. https:\/\/doi.org\/10.1109\/ISBI.2018.8363675.","DOI":"10.1109\/ISBI.2018.8363675"},{"key":"9528_CR44","doi-asserted-by":"publisher","unstructured":"Scheinost, D., Onofrey, J. A., Kwon, S. H., Cross, S. N., Sze, G., Ment, L. R., & Papademetris, X. (2018). A fetal fMRI specific motion correction algorithm using 2nd order edge features. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 1288\u20131292. https:\/\/doi.org\/10.1109\/ISBI.2018.8363807.","DOI":"10.1109\/ISBI.2018.8363807"},{"issue":"1","key":"9528_CR45","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijdevneu.2011.10.006","volume":"30","author":"V Sch\u00f6pf","year":"2012","unstructured":"Sch\u00f6pf, V., Kasprian, G., Brugger, P. C., & Prayer, D. (2012). Watching the fetal brain at \u2018rest.\u2019. International Journal of Developmental Neuroscience, 30(1), 11\u201317. https:\/\/doi.org\/10.1016\/j.ijdevneu.2011.10.006.","journal-title":"International Journal of Developmental Neuroscience"},{"issue":"8","key":"9528_CR46","doi-asserted-by":"publisher","first-page":"3757","DOI":"10.1007\/s00429-018-1717-y","volume":"223","author":"A-L Schuler","year":"2018","unstructured":"Schuler, A.-L., Bartha-Doering, L., Jakab, A., Schwartz, E., Seidl, R., Kienast, P., Lackner, S., Langs, G., Prayer, D., & Kasprian, G. (2018). Tracing the structural origins of atypical language representation: Consequences of prenatal mirror-imaged brain asymmetries in a dizygotic twin couple. Brain Structure and Function, 223(8), 3757\u20133767. https:\/\/doi.org\/10.1007\/s00429-018-1717-y.","journal-title":"Brain Structure and Function"},{"issue":"3","key":"9528_CR47","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1016\/j.neuroimage.2011.09.062","volume":"59","author":"A Serag","year":"2012","unstructured":"Serag, A., Aljabar, P., Ball, G., Counsell, S. J., Boardman, J. P., Rutherford, M. A., Edwards, A. D., Hajnal, J. V., & Rueckert, D. (2012). Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. NeuroImage, 59(3), 2255\u20132265. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.09.062.","journal-title":"NeuroImage"},{"key":"9528_CR48","doi-asserted-by":"publisher","unstructured":"Serag, A., Macnaught, G., Denison, F. C., Reynolds, R. M., Semple, S. I., & Boardman, J. P. (2017). Histograms of oriented 3D gradients for fully automated fetal brain localization and robust motion correction in 3 T magnetic resonance images. BioMed Research International, 2017. https:\/\/doi.org\/10.1155\/2017\/3956363.","DOI":"10.1155\/2017\/3956363"},{"key":"9528_CR49","doi-asserted-by":"publisher","first-page":"94170N","DOI":"10.1117\/12.2082236","volume":"9417","author":"S Seshamani","year":"2015","unstructured":"Seshamani, S., Blazejewska, A. I., Gatenby, C., Mckown, S., Caucutt, J., Dighe, M., & Studholme, C. (2015). Comparing consistency of R2* and T2*-weighted BOLD analysis of resting state fetal fMRI. Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 9417, 94170N. https:\/\/doi.org\/10.1117\/12.2082236.","journal-title":"Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging"},{"issue":"2","key":"9528_CR50","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.media.2013.10.011","volume":"18","author":"S Seshamani","year":"2014","unstructured":"Seshamani, S., Cheng, X., Fogtmann, M., Thomason, M. E., & Studholme, C. (2014). A Method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain. Medical Image Analysis, 18(2), 285\u2013300. https:\/\/doi.org\/10.1016\/j.media.2013.10.011.","journal-title":"Medical Image Analysis"},{"key":"9528_CR51","doi-asserted-by":"crossref","unstructured":"Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: An automated cortical surface identification tool. Medical Image Analysis, 14.","DOI":"10.1016\/S1361-8415(02)00054-3"},{"issue":"2","key":"9528_CR52","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.neuroimage.2010.02.025","volume":"51","author":"F Shi","year":"2010","unstructured":"Shi, F., Yap, P.-T., Fan, Y., Gilmore, J. H., Lin, W., & Shen, D. (2010). Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation. NeuroImage, 51(2), 684\u2013693. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.02.025.","journal-title":"NeuroImage"},{"key":"9528_CR53","doi-asserted-by":"publisher","unstructured":"Song, L., Mishra, V., Ouyang, M., Peng, Q., Slinger, M., Liu, S., & Huang, H. (2017). Human fetal brain connectome: structural network development from middle fetal stage to birth. Frontiers in Neuroscience, 11. https:\/\/doi.org\/10.3389\/fnins.2017.00561.","DOI":"10.3389\/fnins.2017.00561"},{"issue":"1","key":"9528_CR54","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1146\/annurev-bioeng-071910-124654","volume":"13","author":"C Studholme","year":"2011","unstructured":"Studholme, C. (2011). Mapping fetal brain development in utero using magnetic resonance imaging: the big bang of brain mapping. Annual Review of Biomedical Engineering, 13(1), 345\u2013368. https:\/\/doi.org\/10.1146\/annurev-bioeng-071910-124654.","journal-title":"Annual Review of Biomedical Engineering"},{"issue":"2","key":"9528_CR55","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1053\/j.semperi.2015.01.003","volume":"39","author":"C Studholme","year":"2015","unstructured":"Studholme, C. (2015). Mapping the developing human brain in utero using quantitative MR imaging techniques. Seminars in Perinatology, 39(2), 105\u2013112. https:\/\/doi.org\/10.1053\/j.semperi.2015.01.003.","journal-title":"Seminars in Perinatology"},{"key":"9528_CR56","doi-asserted-by":"publisher","unstructured":"Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15. https:\/\/doi.org\/10.1186\/s12880-015-0068-x.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"9528_CR57","doi-asserted-by":"publisher","unstructured":"Takahashi, E., Folkerth, R. D., Galaburda, A. M., & Grant, P. E. (2012). Emerging cerebral connectivity in the human fetal brain: An MR tractography study. Cerebral Cortex (New York, N.Y.: 1991), 22(2), 455\u2013464. https:\/\/doi.org\/10.1093\/cercor\/bhr126.","DOI":"10.1093\/cercor\/bhr126"},{"issue":"1","key":"9528_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tins.2017.11.004","volume":"41","author":"ME Thomason","year":"2018","unstructured":"Thomason, M. E. (2018). Structured spontaneity: building circuits in the human prenatal brain. Trends in Neurosciences, 41(1), 1\u20133. https:\/\/doi.org\/10.1016\/j.tins.2017.11.004.","journal-title":"Trends in Neurosciences"},{"issue":"5","key":"9528_CR59","doi-asserted-by":"publisher","first-page":"e94423","DOI":"10.1371\/journal.pone.0094423","volume":"9","author":"ME Thomason","year":"2014","unstructured":"Thomason, M. E., Brown, J. A., Dassanayake, M. T., Shastri, R., Marusak, H. A., Hernandez-Andrade, E., Yeo, L., Mody, S., Berman, S., Hassan, S. S., & Romero, R. (2014). Intrinsic functional brain architecture derived from graph theoretical analysis in the human fetus. PLoS One, 9(5), e94423. https:\/\/doi.org\/10.1371\/journal.pone.0094423.","journal-title":"PLoS One"},{"key":"9528_CR60","doi-asserted-by":"crossref","unstructured":"Thomason, M. E., Dassanayake, M. T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A. L., Yeo, L., Mody, S., Hernandez-Andrade, E., Hassan, S. S., Studholme, C., Jeong, J.-W., & Romero, R. (2013). Cross-hemispheric functional connectivity in the human fetal brain. Science Translational Medicine, 5(173). https:\/\/doi.org\/10.1126\/scitranslmed.3004978.","DOI":"10.1126\/scitranslmed.3004978"},{"key":"9528_CR61","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.dcn.2014.09.001","volume":"11","author":"ME Thomason","year":"2015","unstructured":"Thomason, M. E., Grove, L. E., Lozon, T. A., Vila, A. M., Ye, Y., Nye, M. J., Manning, J. H., Pappas, A., Hernandez-Andrade, E., Yeo, L., Mody, S., Berman, S., Hassan, S. S., & Romero, R. (2015). Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero. Developmental Cognitive Neuroscience, 11, 96\u2013104. https:\/\/doi.org\/10.1016\/j.dcn.2014.09.001.","journal-title":"Developmental Cognitive Neuroscience"},{"key":"9528_CR62","doi-asserted-by":"publisher","first-page":"39286","DOI":"10.1038\/srep39286","volume":"7","author":"ME Thomason","year":"2017","unstructured":"Thomason, M. E., Scheinost, D., Manning, J. H., Grove, L. E., Hect, J., Marshall, N., Hernandez-Andrade, E., Berman, S., Pappas, A., Yeo, L., Hassan, S. S., Constable, R. T., Ment, L. R., & Romero, R. (2017). Weak functional connectivity in the human fetal brain prior to preterm birth. Scientific Reports, 7, 39286. https:\/\/doi.org\/10.1038\/srep39286.","journal-title":"Scientific Reports"},{"key":"9528_CR63","unstructured":"Tommasi, T., Patricia, N., Caputo, B., & Tuytelaars, T. (2015). A deeper look at dataset bias. ArXiv:1505.01257 [Cs]. http:\/\/arxiv.org\/abs\/1505.01257."},{"key":"9528_CR64","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2017.04.004","volume":"155","author":"S Tourbier","year":"2017","unstructured":"Tourbier, S., Velasco-Annis, C., Taimouri, V., Hagmann, P., Meuli, R., Warfield, S. K., Bach Cuadra, M., & Gholipour, A. (2017). Automated template-based brain localization and extraction for fetal brain MRI reconstruction. NeuroImage, 155, 460\u2013472. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.04.004.","journal-title":"NeuroImage"},{"issue":"12","key":"9528_CR65","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.tics.2016.10.001","volume":"20","author":"MI van den Heuvel","year":"2016","unstructured":"van den Heuvel, M. I., & Thomason, M. E. (2016). Functional connectivity of the human brain in utero. Trends in Cognitive Sciences, 20(12), 931\u2013939. https:\/\/doi.org\/10.1016\/j.tics.2016.10.001.","journal-title":"Trends in Cognitive Sciences"},{"key":"9528_CR66","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.dcn.2018.02.001","volume":"30","author":"MI van den Heuvel","year":"2018","unstructured":"van den Heuvel, M. I., Turk, E., Manning, J. H., Hect, J., Hernandez-Andrade, E., Hassan, S. S., Romero, R., van den Heuvel, M. P., & Thomason, M. E. (2018). Hubs in the human fetal brain network. Developmental Cognitive Neuroscience, 30, 108\u2013115. https:\/\/doi.org\/10.1016\/j.dcn.2018.02.001.","journal-title":"Developmental Cognitive Neuroscience"},{"key":"9528_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.07.041","author":"L Vasung","year":"2018","unstructured":"Vasung, L., Abaci Turk, E., Ferradal, S. L., Sutin, J., Stout, J. N., Ahtam, B., Lin, P.-Y., & Grant, P. E. (2018). Exploring early human brain development with structural and physiological neuroimaging. NeuroImage. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.07.041.","journal-title":"NeuroImage"},{"issue":"1","key":"9528_CR68","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1001\/jama.2017.19198","volume":"319","author":"A Verghese","year":"2018","unstructured":"Verghese, A., Shah, N. H., & Harrington, R. A. (2018). What this computer needs is a physician: humanism and artificial intelligence. JAMA, 319(1), 19\u201320. https:\/\/doi.org\/10.1001\/jama.2017.19198.","journal-title":"JAMA"},{"key":"9528_CR69","unstructured":"What is a Container? | App Containerization | Docker. (n.d.). Retrieved March 19, 2021, from https:\/\/www.docker.com\/resources\/what-container."},{"key":"9528_CR70","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neuroimage.2014.01.034","volume":"91","author":"R Wright","year":"2014","unstructured":"Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M. A., Hajnal, J. V., Rueckert, D., & Aljabar, P. (2014). Automatic quantification of normal cortical folding patterns from fetal brain MRI. NeuroImage, 91, 21\u201332. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.01.034.","journal-title":"NeuroImage"},{"key":"9528_CR71","unstructured":"Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018). Confounding variables can degrade generalization performance of radiological deep learning models. ArXiv:1807.00431 [Cs, Stat]. http:\/\/arxiv.org\/abs\/1807.00431."},{"key":"9528_CR72","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume":"2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. Computer Vision \u2013 ECCV, 2014, 818\u2013833. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53.","journal-title":"Computer Vision \u2013 ECCV"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09528-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-021-09528-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09528-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T16:42:16Z","timestamp":1672936936000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-021-09528-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["9528"],"URL":"https:\/\/doi.org\/10.1007\/s12021-021-09528-5","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,15]]},"assertion":[{"value":"18 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}