Batchelor, P.G.,
Castellano-Smith, A.D.,
Hill, D.L.G.,
Hawkes, D.J.,
Cox, T.C.S.,
Dean, A.F.,
Measures of folding applied to the development of the human fetal brain,
MedImg(21), No. 8, August 2002, pp. 953-965.
IEEE Top Reference.
0301
BibRef
Thomaz, C.E.,
Boardman, J.P.,
Counsell, S.,
Hill, D.L.G.,
Hajnal, J.V.,
Edwards, A.D.,
Rutherford, M.A.,
Gillies, D.F.,
Rueckert, D.,
A multivariate statistical analysis of the developing human brain in
preterm infants,
IVC(25), No. 6, 1 June 2007, pp. 981-994.
Elsevier DOI
0704
Multivariate statistics; Small sample size; Brain images; Preterm infants
BibRef
Pienaar, R.,
Fischl, B.,
Caviness, V.,
Makris, N.,
Grant, P.E.,
A methodology for analyzing curvature in the developing brain from
preterm to adult,
IJIST(18), No. 1, 2008, pp. 42-68.
DOI Link
0806
BibRef
Mutsvangwa, T.E.M.,
Smit, J.,
Hoyme, H.E.,
Kalberg, W.,
Viljoen, D.L.,
Meintjes, E.M.,
Douglas, T.S.,
Design, Construction, and Testing of a Stereo-Photogrammetric Tool for
the Diagnosis of Fetal Alcohol Syndrome in Infants,
MedImg(28), No. 9, September 2009, pp. 1448-1458.
IEEE DOI
0909
BibRef
Douglas, T.S.[Tania S.],
Martinez, F.[Fernando],
Meintjes, E.M.[Ernesta M.],
Vaughan, C.L.[Christopher L.],
Viljoen, D.L.[Denis L.],
A Photogrammetric Method for the Assessment of Facial Morphology in
Fetal Alcohol Syndrome Screening,
PCV02(B: 48).
0305
BibRef
Aljabar, P.,
Wolz, R.,
Srinivasan, L.,
Counsell, S.J.,
Rutherford, M.A.,
Edwards, A.D.,
Hajnal, J.V.,
Rueckert, D.,
A Combined Manifold Learning Analysis of Shape and Appearance to
Characterize Neonatal Brain Development,
MedImg(30), No. 12, December 2011, pp. 2072-2086.
IEEE DOI
1112
BibRef
Zhu, H.,
Styner, M.[Martin],
Tang, N.,
Liu, Z.,
Lin, W.,
Gilmore, J.H.[John H.],
FRATS: Functional Regression Analysis of DTI Tract Statistics,
MedImg(29), No. 4, April 2010, pp. 1039-1049.
IEEE DOI
1003
BibRef
Xu, S.[Shun],
Styner, M.[Martin],
Gilmore, J.H.[John H.],
Piven, J.[Joseph],
Gerig, G.[Guido],
Multivariate nonlinear mixed model to analyze longitudinal image data:
MRI study of early brain development,
MMBIA08(1-8).
IEEE DOI
0806
See also Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data.
BibRef
Jespersen, S.N.,
Leigland, L.A.,
Cornea, A.,
Kroenke, C.D.,
Determination of Axonal and Dendritic Orientation Distributions Within
the Developing Cerebral Cortex by Diffusion Tensor Imaging,
MedImg(31), No. 1, January 2012, pp. 16-32.
IEEE DOI
1201
BibRef
Serag, A.,
Kyriakopoulou, V.,
Rutherford, M.A.,
Edwards, A.D.,
Hajnal, J.V.,
Aljabar, P.,
Counsell, S.J.,
Boardman, J.P.,
Rueckert, D.,
A Multi-channel 4D Probabilistic Atlas of the Developing Brain:
Application to Fetuses and Neonates,
BMVA(2012), No. 3, 2012, pp. 1-14.
PDF File.
1209
BibRef
Makropoulos, A.,
Gousias, I.S.,
Ledig, C.,
Aljabar, P.,
Serag, A.,
Hajnal, J.V.,
Edwards, A.D.,
Counsell, S.J.,
Rueckert, D.,
Automatic Whole Brain MRI Segmentation of the Developing Neonatal
Brain,
MedImg(33), No. 9, September 2014, pp. 1818-1831.
IEEE DOI
1410
biomedical MRI
BibRef
Zhang, Y.,
Shi, F.,
Wu, G.,
Wang, L.,
Yap, P.T.,
Shen, D.,
Consistent Spatial-Temporal Longitudinal Atlas Construction for
Developing Infant Brains,
MedImg(35), No. 12, December 2016, pp. 2568-2577.
IEEE DOI
1612
Brain modeling
BibRef
Hong, Y.,
Kim, J.,
Chen, G.,
Lin, W.,
Yap, P.,
Shen, D.,
Longitudinal Prediction of Infant Diffusion MRI Data via Graph
Convolutional Adversarial Networks,
MedImg(38), No. 12, December 2019, pp. 2717-2725.
IEEE DOI
1912
Convolution, Magnetic resonance imaging, Laplace equations,
Chebyshev approximation, Training, Generators, Loss measurement,
early brain development
BibRef
Zille, P.,
Calhoun, V.D.,
Stephen, J.M.,
Wilson, T.W.,
Wang, Y.,
Fused Estimation of Sparse Connectivity Patterns From Rest fMRI:
Application to Comparison of Children and Adult Brains,
MedImg(37), No. 10, October 2018, pp. 2165-2175.
IEEE DOI
1810
Correlation, Sparse matrices, Estimation, Matrix decomposition,
Brain, Data mining, Time series analysis, Sparse models,
brain development
BibRef
Zhang, C.,
Adeli, E.,
Wu, Z.,
Li, G.,
Lin, W.,
Shen, D.,
Infant Brain Development Prediction With Latent Partial Multi-View
Representation Learning,
MedImg(38), No. 4, April 2019, pp. 909-918.
IEEE DOI
1904
Data models, Brain modeling, Pediatrics, Predictive models,
Task analysis, Magnetic resonance imaging,
multi-view learning
BibRef
Zhang, A.,
Cai, B.,
Hu, W.,
Jia, B.,
Liang, F.,
Wilson, T.W.,
Stephen, J.M.,
Calhoun, V.D.,
Wang, Y.,
Joint Bayesian-Incorporating Estimation of Multiple Gaussian
Graphical Models to Study Brain Connectivity Development in
Adolescence,
MedImg(39), No. 2, February 2020, pp. 357-365.
IEEE DOI
2002
Aldolescence, fMRI, brain development,
brain functional connectivity,
joint estimation
BibRef
Içer, S.[Semra],
Functional connectivity differences in brain networks from childhood
to youth,
IJIST(30), No. 1, 2020, pp. 75-91.
DOI Link
2002
age-related brain maturation, healthy childhood development, resting-state networks
BibRef
Pasban, S.[Sadegh],
Mohamadzadeh, S.[Sajad],
Zeraatkar-Moghaddam, J.[Javad],
Shafiei, A.K.[Amir Keivan],
Infant brain segmentation based on a combination of VGG-16 and U-Net
deep neural networks,
IET-IPR(14), No. 17, 24 December 2020, pp. 4756-4765.
DOI Link
2104
BibRef
Cheng, J.[Jiale],
Zhang, X.[Xin],
Ni, H.[Hao],
Li, C.Y.[Chen-Yang],
Xu, X.M.[Xiang-Min],
Wu, Z.W.[Zheng-Wang],
Wang, L.[Li],
Lin, W.[Weili],
Li, G.[Gang],
Path Signature Neural Network of Cortical Features for Prediction of
Infant Cognitive Scores,
MedImg(41), No. 7, July 2022, pp. 1665-1676.
IEEE DOI
2207
Feature extraction, Machine learning, Data models, Cognition,
Brain modeling, Biological neural networks, Pediatrics,
path signature features
BibRef
Li, Y.[Yu],
Zhang, X.[Xin],
Nie, J.X.[Jing-Xin],
Zhang, G.W.[Guo-Wei],
Fang, R.Y.[Rui-Yan],
Xu, X.M.[Xiang-Min],
Wu, Z.W.[Zheng-Wang],
Hu, D.[Dan],
Wang, L.[Li],
Zhang, H.[Han],
Lin, W.[Weili],
Li, G.[Gang],
Brain Connectivity Based Graph Convolutional Networks and Its
Application to Infant Age Prediction,
MedImg(41), No. 10, October 2022, pp. 2764-2776.
IEEE DOI
2210
Brain modeling, Feature extraction, Predictive models, Convolution,
Task analysis, Deep learning, Data models, Age prediction, rs-fMRI
BibRef
Zhang, X.[Xuzhe],
He, X.Z.[Xin-Zi],
Guo, J.[Jia],
Ettehadi, N.[Nabil],
Aw, N.[Natalie],
Semanek, D.[David],
Posner, J.[Jonathan],
Laine, A.[Andrew],
Wang, Y.[Yun],
PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI
Synthesizer Based on Transformers,
MedImg(41), No. 10, October 2022, pp. 2925-2940.
IEEE DOI
2210
6G mobile communication, Licenses, Hafnium, Kernel, Infant brain MRI,
MRI synthesis, neural network, performer, transformer
BibRef
Sun, L.[Liang],
Shao, W.[Wei],
Zhu, Q.[Qi],
Wang, M.L.[Mei-Ling],
Li, G.[Gang],
Zhang, D.[Daoqiang],
Multi-scale multi-hierarchy attention convolutional neural network
for fetal brain extraction,
PR(133), 2023, pp. 109029.
Elsevier DOI
2210
Fetal brain extraction, In utero MR images, Multi-scale,
Multi-hierarchy, 3D convolutional neural network
BibRef
O'Reilly, C.[Christian],
Plamondon, R.[Réjean],
Faci, N.[Nadir],
The Lognometer: A New Normalized and Computerized Device for
Assessing the Neurodevelopment of Fine Motor Control in Children,
ICPR22(952-958)
IEEE DOI
2212
Motor drives, Pediatrics, Sensitivity, Parameter estimation,
Sociology, Trajectory, Neuromotor development,
Parameter Extraction
BibRef
Gao, Y.[Yuan],
Lee, L.[Lokhin],
Droste, R.[Richard],
Craik, R.[Rachel],
Beriwal, S.[Sridevi],
Papageorghiou, A.[Aris],
Noble, A.[Alison],
A Dual Adversarial Calibration Framework for Automatic Fetal Brain
Biometry,
CVAMD21(3239-3247)
IEEE DOI
2112
Adaptation models, Ultrasonic imaging, Head,
Biological system modeling, Perturbation methods, Point of care
BibRef
Rampun, A.[Andrik],
Jarvis, D.[Deborah],
Griffiths, P.[Paul],
Armitage, P.[Paul],
Fetal Brain Segmentation Using Convolutional Neural Networks with
Fusion Strategies,
ISVC20(II:113-124).
Springer DOI
2103
BibRef
Fishbaugh, J.[James],
Styner, M.[Martin],
Grewen, K.[Karen],
Gilmore, J.[John],
Gerig, G.[Guido],
Spatiotemporal Modeling for Image Time Series with Appearance Change:
Application to Early Brain Development,
MFCA19(174-185).
Springer DOI
1912
BibRef
Meng, Y.[Yu],
Li, G.[Gang],
Gao, Y.Z.[Yao-Zong],
Gilmore, J.H.[John H.],
Lin, W.[Weili],
Shen, D.G.[Ding-Gang],
Subject-Specific Estimation of Missing Cortical Thickness Maps in
Developing Infant Brains,
MCV15(83-92).
Springer DOI
1608
BibRef
Alansary, A.,
Soliman, A.,
Nitzken, M.,
Khalifa, F.,
Elnakib, A.,
Mostapha, M.,
Casanova, M.F.,
El-Baz, A.,
An integrated geometrical and stochastic approach for accurate infant
brain extraction,
ICIP14(3542-3546)
IEEE DOI
1502
Brain modeling
BibRef
Lanche, S.[Stéphanie],
Darvann, T.A.[Tron A.],
Ólafsdóttir, H.[Hildur],
Hermann, N.V.[Nuno V.],
van Pelt, A.E.[Andrea E.],
Govier, D.[Daniel],
Tenenbaum, M.J.[Marissa J.],
Naidoo, S.[Sybill],
Larsen, P.[Per],
Kreiborg, S.[Sven],
Larsen, R.[Rasmus],
Kane, A.A.[Alex A.],
A Statistical Model of Head Asymmetry in Infants with Deformational
Plagiocephaly,
SCIA07(898-907).
Springer DOI
0706
BibRef
Yu, P.[Peng],
Yeo, B.T.T.[Boon Thye Thomas],
Grant, P.E.[P. Ellen],
Fischl, B.[Bruce],
Golland, P.[Polina],
Cortical Folding Development Study based on Over-Complete Spherical
Wavelets,
MMBIA07(1-8).
IEEE DOI
0710
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, General Segmentation Issues .