Abstract
To accurately chart the dynamic brain developmental trajectories in infants, many longitudinal neuroimaging studies prefer having a complete dataset. Unfortunately, missing data at certain time points are unavoidable in longitudinal datasets. To better use incomplete longitudinal data, we propose a novel method to estimate the subject-specific vertex-wise cortical thickness maps at missing time points, by using a customized regression forest, Dynamically-Assembled Regression Forest (DARF). DARF ensures spatial smoothness of the estimated cortical thickness maps and also the computational efficiency. The proposed method can fully exploit the available information from the subjects both with and without missing scans. Our method has been applied to estimate the missing cortical thickness maps in a longitudinal infant dataset, which includes 31 healthy subjects, with each having up to 5 scans. The experimental results indicate that our method can accurately estimate missing cortical thickness maps, with the average vertex-wise error less than 0.23 mm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gilmore, J.H., Shi, F., Woolson, S.L., Knickmeyer, R.C., Short, S.J., Lin, W., Zhu, H., Hamer, R.M., Styner, M., Shen, D.: Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012)
Li, G., Nie, J., Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23, 2724–2733 (2013)
Li, G., Nie, J., Wang, L., Shi, F., Lyall, A.E., Lin, W., Gilmore, J.H., Shen, D.: Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age. Cereb. Cortex 24, 1289–1300 (2014)
Meng, Y., Li, G., Lin, W., Gilmore, J.H., Shen, D.: Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants. NeuroImage 100, 206–218 (2014)
Nie, J., Li, G., Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Longitudinal development of cortical thickness, folding, and fiber density networks in the first 2 years of life. Hum. Brain Mapp. 35, 3726–3737 (2014)
Awate, S.P., Yushkevich, P.A., Song, Z., Licht, D.J., Gee, J.C.: Cerebral cortical folding analysis with multivariate modeling and testing: studies on gender differences and neonatal development. Neuroimage 53, 450–459 (2010)
Dubois, J., Benders, M., Borradori-Tolsa, C., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S.V., Warfield, S.K., Mangin, J.F., Huppi, P.S.: Primary cortical folding in the human newborn: an early marker of later functional development. Brain: J. Neurol. 131, 2028–2041 (2008)
Gilmore, J.H., Lin, W., Prastawa, M.W., Looney, C.B., Vetsa, Y.S., Knickmeyer, R.C., Evans, D.D., Smith, J.K., Hamer, R.M., Lieberman, J.A., Gerig, G.: Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. J. Neurosci. Official J. Soc. Neurosci. 27, 1255–1260 (2007)
Schnack, H.G., van Haren, N.E., Brouwer, R.M., Evans, A., Durston, S., Boomsma, D.I., Kahn, R.S., Hulshoff Pol, H.E.: Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb. Cortex 25, 1608–1617 (2015)
Lyall, A.E., Shi, F., Geng, X., Woolson, S., Li, G., Wang, L., Hamer, R.M., Shen, D., Gilmore, J.H.: Dynamic development of regional cortical thickness and surface area in early childhood. Cereb. Cortex 25, 2204–2212 (2015)
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Tsiporkova, E., Boeva, V.: Two-pass imputation algorithm for missing value estimation in gene expression time series. J. Bioinf. Comput. Biol. 5, 1005–1022 (2007)
Candès, E., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9, 717–772 (2009)
Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Trans. Pattern Anal. Mach. Intell. 35, 208–220 (2013)
Li, G., Nie, J., Wang, L., Shi, F., Gilmore, J.H., Lin, W., Shen, D.: Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage 90, 266–279 (2014)
Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Constructing 4D infant cortical surface atlases based on dynamic developmental trajectories of the cortex. Med. Image Comput. Comput. Assist. Interv. 17, 89–96 (2014)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. USA 97, 11050–11055 (2000)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B (Methodological) 58, 267–288 (1996)
Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16, 265–277 (2012)
Li, G., Wang, L., Shi, F., Lyall, A.E., Lin, W., Gilmore, J.H., Shen, D.: Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age. J. Neurosci. 34, 4228–4238 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Meng, Y., Li, G., Gao, Y., Gilmore, J.H., Lin, W., Shen, D. (2016). Subject-Specific Estimation of Missing Cortical Thickness Maps in Developing Infant Brains. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science(), vol 9601. Springer, Cham. https://doi.org/10.1007/978-3-319-42016-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-42016-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42015-8
Online ISBN: 978-3-319-42016-5
eBook Packages: Computer ScienceComputer Science (R0)