The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth
Open accessAbstract
Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.
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Cites
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,073
AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,669
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
American Journal of Neuroradiology
(2020) · American Journal of Neuroradiology · doi:10.3174/ajnr · Cited by 2,453
Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances
Volpe, J. J. (2009) · The Lancet Neurology · doi:10.1016/s1474-4422(08)70294-1 · Cited by 2,410
Gluckman, P. D., Wyatt, J. S., Azzopardi, D., Ballard, R., Edwards, A. D., Ferriero, D. M., Polin, R. A., Robertson, C. M., Thoresen, M., Whitelaw, A., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17946-x · Cited by 2,270
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Self-citationComputing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
Beg, M. F., Miller, M. I., Trouvé, A., & Younes, L. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000043755.93987.aa · Cited by 1,719
Cited by
Single-cell brain organoid screening identifies developmental defects in autism
Li, C., Fleck, J. S., Martins-Costa, C., Burkard, T. R., Themann, J., Stuempflen, M., Peer, A. M., Vertesy, Á., Littleboy, J. B., Esk, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06473-y · Cited by 283
“Post-processing was conducted in a similar methodology as before (73, 74).”
MethodExploring early human brain development with structural and physiological neuroimaging
Vasung, L., Abaci Turk, E., Ferradal, S. L., Sutin, J., Stout, J. N., Ahtam, B., Lin, P.-Y., & Grant, P. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.041 · Cited by 177
Functional Connectome of the Fetal Brain
Turk, E., van den Heuvel, M. I., Benders, M. J., de Heus, R., Franx, A., Manning, J. H., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., et al. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2891-18.2019 · Cited by 176
Fetal Brain Volume Predicts Neurodevelopment in Congenital Heart Disease
Sadhwani, A., Wypij, D., Rofeberg, V., Gholipour, A., Mittleman, M., Rohde, J., Velasco-Annis, C., Calderon, J., Friedman, K. G., Tworetzky, W., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.056305 · Cited by 169
Self-citationWu, Y., Kapse, K., Jacobs, M., Niforatos-Andescavage, N., Donofrio, M. T., Krishnan, A., Vezina, G., Wessel, D., du Plessis, A., & Limperopoulos, C. (2020) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2019.5316 · Cited by 128
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Metadata
- Authors
- 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.
- Venue
- Scientific Reports
- Published
- 2017-03-28
- Volume
- 7
- Issue
- 1
- Pages
- 476-476
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2600904149
- Cited by
- 363
- References
- 83
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- gold
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- 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. https://doi.org/10.1038/s41598-017-00525-w
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