Abstract
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject’s health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient’s scans to the predicted subject-specific healthy anatomical trajectory.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)
Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. Int. J. Comp. Vis. 90(2), 255–266 (2010)
Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1), 22–59 (2013)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Ge, T., Nichols, T.E., Ghosh, D., Mormino, E.C., Smoller, J.W., Sabuncu, M.R., et al.: A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application. NeuroImage (2015)
Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., L Whitwell, J., Ward, C., et al.: The alzheimer’s disease neuroimaging initiative (adni): Mri methods. Journal of Magnetic Resonance Imaging 27(4), 685–691 (2008)
Kimeldorf, G., Wahba, G.: Some results on tchebycheffian spline functions. Journal of Mathematical Analysis and Applications 33(1), 82–95 (1971)
Liu, D., Lin, X., Ghosh, D.: Semiparametric regression of multidimensional genetic pathway data: Least-squares kernel machines and linear mixed models. Biometrics 63(4), 1079–1088 (2007)
McCullagh, P.: Generalized linear models. European Journal of Operational Research 16(3), 285–292 (1984)
McCulloch, C.E., Neuhaus, J.M.: Generalized linear mixed models. Wiley Online Library (2001)
Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in mci patients, and their use in prediction of short-term conversion to ad: results from adni. Neuroimage 44(4), 1415–1422 (2009)
Pfefferbaum, A., Rohlfing, T., Rosenbloom, M.J., Chu, W., Colrain, I.M., Sullivan, E.V.: Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of mri. Neuroimage 65, 176–193 (2013)
Queller, D.C., Goodnight, K.F.: Estimating relatedness using genetic markers. Evolution, 258–275 (1989)
Rohlfing, T., Sullivan, E.V., Pfefferbaum, A.: Regression models of atlas appearance. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 151–162. Springer, Heidelberg (2009)
Sadeghi, N., Prastawa, M., Fletcher, P.T., Vachet, C., Wang, B.: et al.: Multivariate modeling of longitudinal mri in early brain development with confidence measures. In: 2013 IEEE Inter. Symp. Biomed. Imag., pp. 1400–1403. IEEE (2013)
Wahba, G.: Spline models for observational data, vol. 59. SIAM (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dalca, A.V., Sridharan, R., Sabuncu, M.R., Golland, P. (2015). Predictive Modeling of Anatomy with Genetic and Clinical Data. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-24574-4_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24573-7
Online ISBN: 978-3-319-24574-4
eBook Packages: Computer ScienceComputer Science (R0)