Abstract
One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer’s disease (AD), which manifest themselves in the same at-risk population. We evaluate a novel symmetric diffeomorphic image registration method for automatically providing detailed anatomical measurement over the aged and neurodegenerative brain. Our evaluation will compare gold standard, human segmentation with our method’s atlas-based segmentation of the cerebral cortex, cerebellum and the frontal lobe. The new method compares favorably to an open-source, previously evaluated implementation of Thirion’s Demons algorithm.
Keywords
- Autism Spectrum Disorder
- Frontal Lobe
- Image Registration
- Frontotemporal Dementia
- Deformable Image Registration
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.
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References
Ratnavalli, E., Brayne, C., Dawson, K., Hodges, J.: The prevalence of frontotemporal dementia. Neurology 58, 1585–1586 (2002)
Fox, N., Crum, W., Scahill, R., Stevens, J., Janssen, J., Rossor, M.: Imaging of onset and progression of alzheimer’s disease with voxel-compression mapping of serial magnetic resonance images. Lancet 358, 201–205 (2001)
Studholme, C., Cardenas, V., Blumenfeld, R., Schuff, N., Rosen, H.J., Miller, B., Weiner, M.: Deformation tensor morphometry of semantic dementia with quantitative validation. Neuroimage 21, 1387–1398 (2004)
Sparks, B., Friedman, S., Shaw, D., Aylward, E., Echelard, D., Artru, A., Maravilla, K., Giedd, J., Munson, J., Dawson, G., Dager, S.: Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59, 184–192 (2002)
Dawant, B., Li, R., Cetinkaya, E., Kao, C., Fitzpatrick, J., Konrad, P.: Computerized atlas-guided positioning of deep brain simulators: A feasibility study. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 142–150. Springer, Heidelberg (2003)
Avants, B., Epstein, C.L., Gee, J.C.: Geodesic image interpolation: Parameterizing and interpolating spatiotemporal images. In: ICCV Workshop on Variational and Level Set Methods, pp. 247–258 (in press, 2005)
Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D., Evans, A., Malandain, G., Ayache, N., Christensen, G., Johnson, H.: Retrospective evaluation of inter-subject brain registration. IEEE Transactions on Medical Imaging 22, 1120–1130 (2003)
Thirion, J.: Non-rigid matching using demons. IEEE Computer Vision and Pattern Recognition, 245–251 (1996)
Dawant, B., Hartmann, S., Thirion, J.P., Maes, F., Vandermeulen, D., Demaerel, P.: Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations, part II: methodology and validation on severely atrophied brains. IEEE Trans. Med. Imaging 18, 926–971 (1999)
Trouve, A.: Diffeomorphism groups and pattern matching in image analysis. Intl. J. Comp. Vis. 28, 213–221 (1998)
Miller, M., Trouve, A., Younes, L.: On the metrics and Euler-Lagrange equations of computational anatomy. Annu. Rev. Biomed. Eng. 4, 375–405 (2002)
Beg, F., Miller, M., Trouve, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comp. Vision 61, 139–157 (2005)
Avants, B., Schoenemann, P.T., Gee, J.C.: Landmark and intensity-driven lagrangian frame diffeomorphic image registration: Application to structurally and functionally based inter-species comparison. In: Medical Image Analysis. e-pub. (in press, 2005)
Arnold, V.I.: Ordinary Differential Equations. Springer, Berlin (1991)
Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: A variational approach to multi-modal image matching. Intl. J. Comp. Vis. 50, 329–343 (2002)
Johnson, H.J., Christensen, G.E.: Consistent landmark and intensity-based image registration. IEEE Trans. Med. Imaging 21, 450–461 (2002)
Yoo, T.: Insight Into Images: Principles and Practice for Segmentation, Registration and Image Analysis. AK Peters, Ltd., Natick (2003)
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Avants, B.B., Grossman, M., Gee, J.C. (2006). Symmetric Diffeomorphic Image Registration: Evaluating Automated Labeling of Elderly and Neurodegenerative Cortex and Frontal Lobe. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_7
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DOI: https://doi.org/10.1007/11784012_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35648-6
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