Abstract
Longitudinal atlas construction is a challenging task in medical image analysis. Given a set of longitudinal images of different subjects, the task is how to construct the unbias longitudinal atlas sequence reflecting the anatomical changes over time. In this paper, a novel longitudinal atlas construction framework is proposed. The main contributions of the proposed method lie in the following aspects: (1) Subject-specific longitudinal information is captured by establishing a robust growth model for each subject. (2) The trajectory constraints are enforced for both subject image sequences and the atlas sequence, and only one transformation is needed for each subject to map its image sequence to the atlas sequence while preserving the temporal correspondence. (3) The longitudinal atlases are estimated by groupwise registration and kernel regression, thus no explicit template is used and the atlases are constructed without introducing bias due to the selection of the explicit template. (4) The proposed method is general, where the number of longitudinal images of each subject and the time points at which the images are taken can be different. The proposed method is evaluated on a longitudinal database and compared with a state-of-the-art longitudinal atlas construction method. Experimental results show that the proposed method achieves more consistent spatial-temporal correspondence as well as higher registration accuracy than the compared method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Crum, W.R., Rueckert, D., Jenkinson, M., Kennedy, D., Smith, S.M.: A framework for detailed objective comparison of non-rigid registration algorithms in neuroimaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 679–686. Springer, Heidelberg (2004)
Davis, B., Fletcher, E., Bullitt, E., Joshi, S.: Population shape regression from random design data. In: ICCV, pp. 1–7 (2007)
Durrleman, S., Pennec, X., Trouvé, A., Gerig, G., Ayache, N.: Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 297–304. Springer, Heidelberg (2009)
Gabriel, H., Yundi, S., Hongtu, Z., Mar, S., Martin, S., Marc, N.: Dti longitudinal atlas construction as an average of growth model. In: STIA (2010)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)
Khan, A., Beg, M.: Representation of time-varying shapes in the large deformation diffeomorphic framework. In: ISBI, pp. 1521–1524 (2008)
Perperidis, D., Mohiaddin, R.H., Rueckert, D.: Spatio-temporal free-form registration of cardiac mri sequences. MedIA 9, 441–456 (2005)
Resnick, S.M., Goldszal, A.F., Davatzikos, C., Golski, S., Kraut, M.A., Metter, E.J., Bryan, R.N., Zonderman, A.B.: One-year age changes in mri brain volumes in older adults. Cerebral Cortex 10, 464–472 (2000)
Shen, D., Davatzikos, C.: Measuring temporal morphological changes robustly in brain mr images via 4-dimensional template warping. NeuroImage 21, 1508–1517 (2004)
Yoon, U., Fonov, V.S., Perusse, D., Evans, A.C., Group, B.D.C.: The effect of template choice on morphometric analysis of pediatric brain data. NeuroImage 45, 769–777 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liao, S., Jia, H., Wu, G., Shen, D. (2011). A Novel Longitudinal Atlas Construction Framework by Groupwise Registration of Subject Image Sequences. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-22092-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22091-3
Online ISBN: 978-3-642-22092-0
eBook Packages: Computer ScienceComputer Science (R0)