Abstract
Longitudinal measurements of cortical thickness is a current hot topic in medical imaging research. Measuring the thickness of the cortex through time is normally hindered by the presence of noise, partial volume (PV) effects and topological defects, but mainly by the lack of a common directionality in the measurement to ensure consistency. In this paper, we propose a 4D pipeline (3D + time) using the Khalimsky cubic complex for the extraction of a topologically correct Laplacian field in an unbiased temporal group-wise space. The thickness at each time point is then obtained by integrating the probabilistic segmentation (transformed to the group-wise space) modulated by the Jacobian determinant of its deformation field through the group-wise Laplacian field. Experiments performed on digital phantoms show that the proposed method improves the time consistency of the thickness measurements with a statistically significant increase in accuracy when compared to two well established 3D techniques and a 3D version of the same method. Furthermore, quantitative analysis on brain MRI data showed that the proposed algorithm is able to retrieve increasingly significant time consistent consistent group differences between the cortical thickness of AD patients and controls.
Chapter PDF
Similar content being viewed by others
Keywords
- Cortical Thickness
- Jacobian Determinant
- Digital Topology
- Probabilistic Segmentation
- Cortical Thickness Measurement
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
Holland, D., Brewer, J.B., Hagler, D.J., Fennema-Notestine, C., Fenema-Notestine, C., Dale, A.M., Initiative, A.D.N.: Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. PNAS 106(49) (2009)
Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. P. Natl. Acad. Sci. USA 97(20), 11050–11055 (2000)
Scott, M.L.J., Bromiley, P.A., Thacker, N., Hutchinson, C.E., Jackson, A.: A fast, model-independent method for cerebral cortical thickness estimation using MRI. Medical Image Analysis 13(2), 269–285 (2009)
Clarkson, M.J., Cardoso, M.J., Ridgway, G.R., Modat, M., Leung, K.K., Rohrer, J.D., Fox, N.C., Ourselin, S.: A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage (2011)
Lohmann, G., Preul, C., Hund-Georgiadis, M.: Morphology-based cortical thickness estimation. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 89–100. Springer, Heidelberg (2003)
Jones, S.E., Buchbinder, B.R., Aharon, I.: Three-dimensional mapping of cortical thickness using Laplace’s equation. Human Brain Mapping 11(1), 12–32 (2000)
Yezzi, A.J., Prince, J.L.: An Eulerian PDE approach for computing tissue thickness. IEEE Transactions on Medical Imaging 22(10), 1332–1339 (2003)
Acosta, O., Bourgeat, P., Zuluaga, M.A., Fripp, J., Salvado, O., Ourselin, S.: Alzheimer’s Disease Neuroimaging Initiative: Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian-Eulerian PDE approach using partial volume maps. Medical Image Analysis 13(5), 730–743 (2009)
Li, Y., Wang, Y., Xue, Z., Shi, F., Lin, W., Shen, D., The Alzheimer’s Disease Neuroimaging Initiative: Consistent 4D cortical thickness measurement for longitudinal neuroimaging study. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 133–142. Springer, Heidelberg (2010)
Cardoso, M.J., Clarkson, M.J., Modat, M., Ourselin, S.: On the extraction of topologically correct thickness measurements using khalimsky’s cubic complex. In: IPMI (2011)
Cardoso, M.J., Clarkson, M.J., Ridgway, G.R., Modat, M., Fox, N.C., Ourselin, S.: Alzheimer’s Disease Neuroimaging Initiative. LoAd: A locally adaptive cortical segmentation algorithm. NeuroImage 56(3), 1386–1397 (2011)
Rohlfing, T., Brandt, R., Menzel, R., Maurer, Jr., C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)
Cointepas, Y., Bloch, I., Garnero, L.: A cellular model for multi-objects multi-dimensional homotopic deformations. Pattern Recognition 34(9), 1785–1798 (2001)
Rocha, K.R., Yezzi Jr., J.L., Prince, J.L.: A hybrid Eulerian-Lagrangian approach for thickness, correspondence, and gridding of annular tissues. IEEE TIP, 72–81 (2005)
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
Cardoso, M.J., Clarkson, M.J., Modat, M., Ourselin, S. (2011). Longitudinal Cortical Thickness Estimation Using Khalimsky’s Cubic Complex. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-23629-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23628-0
Online ISBN: 978-3-642-23629-7
eBook Packages: Computer ScienceComputer Science (R0)