Arroyo, M. S., Hopkin, R. J., Nagaraj, U. D., Kline-Fath, B., & Venkatesan, C. (2019). Fetal brain MRI findings and neonatal outcome of common diagnosis at a tertiary care center. Journal of Perinatology, 39(8), 1072–1077. https://doi.org/10.1038/s41372-019-0407-9
De Asis-Cruz (2022). FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net. Front. Neurosci., 07 June 2022. Sec. Brain Imaging Methods. Volume 16– https://doi.org/10.3389/fnins.2022.887634
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. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116. https://doi.org/10.1038/s41592-018-0235-4
Article CAS PubMed Google Scholar
Huisman, T. A. G. M., Martin, E., Kubik-Huch, R., & Marincek, B. (2002). Fetal magnetic resonance imaging of the brain: Technical considerations and normal brain development. European Radiology, 12(8), 1941–1951. https://doi.org/10.1007/s00330-001-1209-x
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z
Article CAS PubMed Google Scholar
Kalavathi, P., & Prasath, V. B. S. (2016). Methods on Skull Stripping of MRI Head scan Images—a review. Journal of Digital Imaging, 29(3), 365–379. https://doi.org/10.1007/s10278-015-9847-8
Article CAS PubMed Google Scholar
Khan, A., & Haast, R. (2021). Snakebids - BIDS integration into snakemake workflows. https://doi.org/10.5281/ZENODO.4488249
McCarthy, P. (2021). FSLeyes. https://doi.org/10.5281/ZENODO.5576035
Prayer, D., Brugger, P. C., & Prayer, L. (2004). Fetal MRI: Techniques and protocols. Pediatric Radiology, 34(9), 685–693. https://doi.org/10.1007/s00247-004-1246-0
Rajagopalan, V., Deoni, S., Panigrahy, A., & Thomason, M. E. (2021). Is fetal MRI ready for neuroimaging prime time? An examination of progress and remaining areas for development. Developmental Cognitive Neuroscience, 51, 100999. https://doi.org/10.1016/j.dcn.2021.100999
Article PubMed PubMed Central Google Scholar
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In N. Navab, J. Hornegger, W. Wells, & A. Frangi (Eds.), Medical Image Computing and Computer-Assisted intervention – MICCAI 2015 (9351 vol.). Cham: Springer. Lecture Notes in Computer Sciencehttps://doi.org/10.1007/978-3-319-24574-4_28
Rousseau, F., Glenn, O. A., Iordanova, B., Rodriguez-Carranza, C., Vigneron, D. B., Barkovich, J. A., & Studholme, C. (2006). Registration-Based Approach for Reconstruction of High-Resolution in Utero fetal MR brain images. Academic Radiology, 13(9), 1072–1081. https://doi.org/10.1016/j.acra.2006.05.003
Rutherford, S., Sturmfels, P., Angstadt, M., Hect, J., Wiens, J., van den Heuvel, M. I., Scheinost, D., Sripada, C., & Thomason, M. (2021). Automated brain masking of fetal functional MRI with Open Data. Neuroinformatics. https://doi.org/10.1007/s12021-021-09528-5
Article PubMed PubMed Central Google Scholar
Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: An automated cortical surface identification tool. Medical Image Analysis, 6(2), 129–142. https://doi.org/10.1016/s1361-8415(02)00054-3
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. https://doi.org/10.1002/hbm.10062
Article PubMed PubMed Central Google Scholar
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23(Suppl 1), 208–219. https://doi.org/10.1016/j.neuroimage.2004.07.051
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), 1–10. https://doi.org/10.1371/journal.pone.0094423
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
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, 96–104. https://doi.org/10.1016/j.dcn.2014.09.001
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(February), 108–115. https://doi.org/10.1016/j.dcn.2018.02.001
Article PubMed PubMed Central Google Scholar
Wheelock, M. D., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., Eggebrecht, A. T., & Thomason, M. E. (2019). Sex differences in functional connectivity during fetal brain development. Developmental Cognitive Neuroscience, 36(May 2018), 100632. https://doi.org/10.1016/j.dcn.2019.100632
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