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Bayesian and grAphical Models for Biomedical Imaging

International MICCAI Workshop on Medical Computer Vision

BAMBI 2016, MCV 2016: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging pp 35–45Cite as

Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10081))

Abstract

In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

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Notes

  1. 1.

    www.adni-info.org.

References

  1. Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)

    Article  Google Scholar 

  2. Thung, K.H., Wee, C.Y., Yap, P.T., Shen, D., Initiative, A.D.N., et al.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage 91, 386–400 (2014)

    Article  Google Scholar 

  3. Thung, K.-H., Yap, P.-T., Adeli-M, E., Shen, D.: Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (PMCI) using low-rank subspace clustering and matrix completion. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 527–534. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_63

    Chapter  Google Scholar 

  4. Liu, M., Zhang, D., Shen, D.: View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum. Brain Mapp. 36(5), 1847–1865 (2015)

    Article  Google Scholar 

  5. Liu, M., Zhang, D., Adeli-Mosabbeb, E., Shen, D.: Inherent structure based multi-view learning with multi-template feature representation for Alzheimer’s disease diagnosis. IEEE Trans. Biomed. Eng. 63(7), 1473–1482 (2016)

    Article  Google Scholar 

  6. Liu, M., Zhang, D., Shen, D.: Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Trans. Med. Imaging 35(6), 1463–1474 (2016)

    Article  Google Scholar 

  7. Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C., Initiative, A.D.N., et al.: Spatially augmented lpboosting for ad classification with evaluations on the adni dataset. Neuroimage 48(1), 138–149 (2009)

    Article  Google Scholar 

  8. Zhu, X., Suk, H.I., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage 100, 91–105 (2014)

    Article  Google Scholar 

  9. Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging Behav. 10(3), 1–11 (2015)

    Google Scholar 

  10. Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)

    Article  Google Scholar 

  11. Gerardin, E., Chételat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.S., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al.: Multidimensional classification of hippocampal shape features discriminates alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4), 1476–1486 (2009)

    Article  Google Scholar 

  12. Gao, Y., Adeli-M., E., Kim, M., Giannakopoulos, P., Haller, S., Shen, D.: Medical image retrieval using multi-graph learning for MCI diagnostic assistance. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 86–93. Springer, Cham (2015). doi:10.1007/978-3-319-24571-3_11

    Chapter  Google Scholar 

  13. Gao, Y., Wee, C.-Y., Kim, M., Giannakopoulos, P., Montandon, M.-L., Haller, S., Shen, D.: MCI identification by joint learning on multiple MRI data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 78–85. Springer, Cham (2015). doi:10.1007/978-3-319-24571-3_10

    Chapter  Google Scholar 

  14. Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008)

    Article  Google Scholar 

  15. Chincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., et al.: Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage 125, 834–847 (2016)

    Article  Google Scholar 

  16. Jack, C., Shiung, M., Gunter, J., Obrien, P., Weigand, S., Knopman, D.S., Boeve, B.F., Ivnik, R.J., Smith, G.E., Cha, R., et al.: Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62(4), 591–600 (2004)

    Article  Google Scholar 

  17. Aguilar, C., Muehlboeck, J.S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Soininen, H., Lovestone, S., Wahlund, L.O., Simmons, A., et al.: Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the addneuromed cohort. Front. Aging Neurosci. 6, 145 (2014)

    Article  Google Scholar 

  18. Thung, K.H., Wee, C.Y., Yap, P.T., Shen, D.: Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct. Funct. 1–17 (2015)

    Google Scholar 

  19. Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22(2), 324–333 (1998)

    Article  Google Scholar 

  20. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005. CVPR 2005, pp. 886–893. IEEE (2005)

    Google Scholar 

  21. Mardia, K.: Assessment of multinormality and the robustness of Hotelling’s T\(^2\) test. Appl. Stat. 24, 163–171 (1975)

    Article  MathSciNet  Google Scholar 

  22. Zhang, J., Gao, Y., Gao, Y., Brent, M., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 35(12), 2524–2533 (2016)

    Google Scholar 

  23. Gao, Y., Shen, D.: Context-aware anatomical landmark detection: application to deformable model initialization in prostate CT images. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 165–173. Springer, Cham (2014). doi:10.1007/978-3-319-10581-9_21

    Google Scholar 

  24. Zhang, J., Gao, Y., Wang, L., Tang, Z., Xia, J.J., Shen, D.: Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multi-scale statistical features. IEEE Trans. Biomed. Eng. 63(9), 1820–1829 (2016)

    Google Scholar 

  25. Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vision 43(1), 29–44 (2001)

    Article  MATH  Google Scholar 

  26. Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006). doi:10.1007/11744085_38

    Chapter  Google Scholar 

  27. Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Multimedia Information Retrieval, pp. 197–206. ACM (2007)

    Google Scholar 

  28. Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, ACM 494–501(2007)

    Google Scholar 

  29. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A k-means clustering algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA

    Jun Zhang, Mingxia Liu, Le An, Yaozong Gao & Dinggang Shen

  2. Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC, USA

    Yaozong Gao

  3. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

    Dinggang Shen

Authors
  1. Jun Zhang
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  2. Mingxia Liu
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  3. Le An
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  4. Yaozong Gao
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  5. Dinggang Shen
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Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

  1. HES-SO, Sierre, Switzerland

    Henning Müller

  2. Siemens AG, Munich, Germany

    B. Michael Kelm

  3. McGill University, Montreal, Québec, Canada

    Tal Arbel

  4. University of Sydney, Sydney, New South Wales, Australia

    Weidong Cai

  5. University College London, London, United Kingdom

    M. Jorge Cardoso

  6. Medical University of Vienna, Vienna, Austria

    Georg Langs

  7. Technische Universität München, Garching, Germany

    Bjoern Menze

  8. Rutgers The State University of New Jersey, Piscataway, New Jersey, USA

    Dimitris Metaxas

  9. University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Albert Montillo

  10. Brigham and Women’s Hospital, Boston, Massachusetts, USA

    William M. Wells III

  11. University of North Carolina at Charlotte, Charlotte, North Carolina, USA

    Shaoting Zhang

  12. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Albert C.S. Chung

  13. University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom

    Mark Jenkinson

  14. IcoMetrix, Leuven, Belgium

    Annemie Ribbens

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Zhang, J., Liu, M., An, L., Gao, Y., Shen, D. (2017). Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-61188-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61187-7

  • Online ISBN: 978-3-319-61188-4

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