Abstract
Neurodegenerative disorders are characterized by changes in multiple biomarkers, which may provide complementary information for diagnosis and prognosis. We present a framework in which proximities derived from random forests are used to learn a low-dimensional manifold from labelled training data and then to infer the clinical labels of test data mapped to this space. The proposed method facilitates the combination of embeddings from multiple datasets, resulting in the generation of a joint embedding that simultaneously encodes information about all the available features. It is possible to combine different types of data without additional processing, and we demonstrate this key feature by application to voxel-based FDG-PET and region-based MR imaging data from the ADNI study. Classification based on the joint embedding coordinates out-performs classification based on either modality alone. Results are impressive compared with other state-of-the-art machine learning techniques applied to multi-modality imaging data.
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References
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Aljabar, P., Rueckert, D., Crum, W.: Automated morphological analysis of magnetic resonance brain imaging using spectral analysis. Neuroimage 43(2), 225–235 (2008)
Wachinger, C., Yigitsoy, M., Navab, N.: Manifold learning for image-based breathing gating with application to 4D ultrasound. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 26–33. Springer, Heidelberg (2010)
Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for non-linear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Gerber, S., Tasdizen, T., Joshi, S., Whitaker, R.: On the manifold structure of the space of brain images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 305–312. Springer, Heidelberg (2009)
Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S., Boardman, J.P., Murgasova, M., Doria, V., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: Combining morphological information in a manifold learning framework: Application to neonatal MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 1–8. Springer, Heidelberg (2010)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Cox, T.F., Cox, M.A.A.: Multidimensional scaling. Chapman and Hall, Boca Raton (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Heidelberg (2011); corrected 5th printing
Shi, T., Horvath, S.: Unsupervised learning with random forest predictors. J. Comp. Graph. Stat. 15(1), 118–138 (2006)
Zhang, D., Wang, Y., Zhou, L., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3), 856–867 (2011)
Hinrichs, C., Singh, V., Xu, G., et al.: Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55(2), 574–589 (2011)
Heckemann, R.A., Keihaninejad, S., Aljabar, P., et al.: Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage 51(1), 221–227 (2010)
Heckemann, R.A., Keihaninejad, S., Aljabar, P., et al.: Automatic morphometry in Alzheimer’s disease and mild cognitive impairment. Neuroimage 56(4), 2024–2037 (2011)
Clarkson, M.J., Ourselin, S., Nielsen, C., et al.: Comparison of phantom and registration scaling corrections using the ADNI cohort. Neuroimage 47(4), 1506–1513 (2009)
Joshi, A., Koeppe, R.A., Fessler, J.A.: Reducing between scanner differences in multi-center PET studies. Neuroimage 46(1), 154–159 (2009)
Yakushev, I., Hammers, A., Fellgiebel, A., et al.: SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage 44(1), 43–50 (2009)
Breiman, L., Friedman, J.H., Olshen, R.A., et al.: Classification and regression trees. Wadsworth, Belmont (1984)
Hampel, H., Burger, K., Teipel, S.J., et al.: Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzh. & Dementia 4(1), 38–48 (2008)
Patwardhan, M.B., McCrory, D.C., Matchar, D.B., et al.: Alzheimer disease: operating characteristics of PET – a meta-analysis. Radiology 231(1), 73–80 (2004)
Cuingnet, R., Gerardin, E., Tessieras, J., et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2), 766–781 (2011)
Ranginwala, N.A., Hynan, L.S., Weiner, M.F., et al.: Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. Am. J. Geriat. Psychiatry 16(5), 384–388 (2008)
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Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D. (2011). Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_20
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DOI: https://doi.org/10.1007/978-3-642-24319-6_20
Publisher Name: Springer, Berlin, Heidelberg
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