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Detection and Classification of Hippocampal Structural Changes in MR Images as a Biomarker for Alzheimer’s Disease

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10960))

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Abstract

Alzheimer’s disease (AD) is the most common form of dementia, comprising around 60% of all dementia cases and affecting 20% of the population over 80 years of age. AD may affect people in different ways. The most common symptom pattern begins with a gradually worsening ability to remember new information, difficulty to solve problems and perform familiar tasks at home, confusion about time or place, and trouble understanding visual images. Currently, the volume reduction of the two hippocampi is the most used structural magnetic resonance imaging (MRI) biomarker of AD. However, despite its clinical use, hippocampal volume reduction is involved not only in AD but also in other dementias and even in healthy aging. In this study, we propose a new computational framework for the detection and classification of hippocampal structural changes in MR images as a biomarker for AD. First, we built a probabilistic atlas of 3D salient points using a dataset of healthy brain images. Then, we detected 3D salient points in a training dataset with cognitively normal (CN) and mild-AD brain images and used them to label each point on the atlas. Next, the 3D salient points detected in each image from the training dataset were matched against the labeled points in the atlas, and their descriptor vectors were used to train a support vector machine with radial basis function (SVM-RBF). Last, we detected 3D salient points, extracted their descriptor vectors, matched them against the atlas and classified them using the SVM-RBF classifier, for each image from the testing dataset. Finally, we attribute a class label (CN/mild-AD) according to the majority of points classified in the corresponding class. We tested our proposed framework using a stratified age group image dataset (551 MR images in total) and assessed the results using a 10-fold cross-validation and ROC methodology. The highest accuracy value achieved by our method was 85% (up to 82.59% sensitivity and 88.50% specificity) for the age group 70–89, and the highest area under the curve was 0.9227.

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Notes

  1. 1.

    http://www.brain-development.org.

  2. 2.

    http://adni.loni.usc.edu/about/centers-cores/study-sites/.

  3. 3.

    http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

References

  1. Chan, M.: Dementia: a public health priority. World Health Organization and Alzheimer’s Disease International (2017)

    Google Scholar 

  2. Fjell, A.M., McEvoy, L., Holland, D., Dale, A.M., Walhovd, K.B.: What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog. Neurobiol. 117, 20–40 (2014)

    Article  Google Scholar 

  3. Guo, H., Song, X., Vandorpe, R., Zhang, Y., Chen, W., Zhang, N., Schmidt, M.H., Rockwood, K.: Evaluation of common structural brain changes in aging and Alzheimer disease with the use of an MRI-based brain atrophy and lesion index: a comparison between T1WI and T2WI at 1.5T and 3T. Brain 35(3), 504–512 (2014)

    Google Scholar 

  4. Abbott, A.: Dementia: a problem for our age. Nature 475(7355), S2–S4 (2011)

    Article  Google Scholar 

  5. Narayanan, L., Murray, A.D.: What can imaging tell us about cognitive impairment and dementia? World J. Radiol. 8(3), 240–254 (2016)

    Article  Google Scholar 

  6. Frisoni, G.B., Fox, N.C., Jack-Jr, 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 

  7. Dolek, N., Saylisoy, S., Ozbabalik, D., Adapinar, B.: Comparison of hippocampal volume measured using magnetic resonance imaging in Alzheimer’s disease, vascular dementia, mild cognitive impairment and pseudodementia. J. Int. Med. Res. 20(2), 717–725 (2012)

    Article  Google Scholar 

  8. De Leon, M., George, A., Stylopoulos, L., Smith, G., Miller, D.: Early marker for Alzheimer’s disease: the atrophic hippocampus. Lancet 334(8664), 672–673 (1989)

    Article  Google Scholar 

  9. Devanand, D.P., Bansal, R., Liu, J., Hao, X., Pradhaban, G., Peterson, B.S.: MRI hippocampal and entorhinal cortex mapping in predicting conversion to Alzheimer’s disease. Neuroimage 60(3), 1622–1629 (2012)

    Article  Google Scholar 

  10. Eskildsen, S.F., Coupé, P., Fonov, V.S., Pruessner, J.C., Collins, D.L.: Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiol. Aging 36(1), S23–S31 (2015)

    Article  Google Scholar 

  11. Halle, M., Talos, I.F., Jakab, M., Makris, N., Meier, D., Wald, L., Fischl, B., Kikinis, R.: Multi-modality MRI-based atlas of the brain. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, Technical report, Surgical Planning Laboratory, Department of Radiology (2017)

    Google Scholar 

  12. Jack, C.R.J., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Krueger, G., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2017)

    Article  Google Scholar 

  13. Teverovskiy, L.A., Becker, J.T., Lopez, O.L., Liu, Y.: Quantified brain asymmetry for age estimation of normal and AD/MCI subjects. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, pp. 1509–1512. IEEE (2008)

    Google Scholar 

  14. Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)

    Article  MathSciNet  Google Scholar 

  15. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  16. Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)

    Article  Google Scholar 

  17. Ourselin, S., Stefanescu, R., Pennec, X.: Robust registration of multi-modal images: towards real-time clinical applications. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 140–147. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45787-9_18

    Chapter  Google Scholar 

  18. Villa-Pinto, C.H., Ferrari, R.J.: Initialization of deformable models in 3D magnetic resonance images guided by automatically detected phase congruency point landmarks. Pattern Recogn. Lett. 79, 1–7 (2016)

    Article  Google Scholar 

  19. Morrone, M.C., Ross, J., Burr, D.C., Owens, R.: Mach bands are phase dependent. Nature 324(6094), 250–253 (1986)

    Article  Google Scholar 

  20. Venkatesh, S., Owens, R.: On the classification of image features. Pattern Recogn. Lett. 11(5), 339–349 (1990)

    Article  Google Scholar 

  21. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. Pattern Anal. Mach. 24(4), 509–522 (2002)

    Article  Google Scholar 

  22. Mortensen, E.N., Deng, H., Shapiro, L.: A SIFT descriptor with global context. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 184–190 (2005)

    Google Scholar 

  23. Carmichael, O.T., Aizenstein, H.A., Davis, S.W., Becker, J.T., Thompson, P.M., Meltzer, C.C., Liu, Y.: Atlas-based hippocampus segmentation in Alzheimer’s disease and mild cognitive impairment. Neuroimage 27(4), 979–990 (2005)

    Article  Google Scholar 

  24. Kalinic, H.: Atlas-based image segmentation: a survey. Technical report, Universiy of Zagreb, Zagreb, Croatia (2008)

    Google Scholar 

  25. Toews, M., Wells-III, W., Collins, D.L., Arbel, T.: Feature-based morphometry: discovering group-related anatomical patterns. Neuroimage 49(3), 2318–2327 (2010)

    Article  Google Scholar 

  26. Vincent, P., Bengio, Y.: Manifold Parzen windows. In: Advances in Neural Information Processing Systems, vol. 15. MIT Press, Cambridge (2003)

    Google Scholar 

  27. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  28. Nell, C., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  29. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, Taiwan, May 2016

    Google Scholar 

  30. Fawcett, T.: An introduction of ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  31. Sørensen, L., Igel, C., Liv Hansen, N., Osler, M., Lauritzen, M., Rostrup, E., Nielsen, M., Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing: Early detection of alzheimer’s disease using MRI hippocampal texture. Hum. Brain Mapp. 37(3), 1148–1161 (2016)

    Google Scholar 

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Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding Statement. The authors would like to thank the São Paulo Research Foundation (FAPESP) (grant numbers 2015/02232-1 and 2014/11988-0) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for the finantial support of this research.

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Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, Rod. Washington Luís, Km 235, Mailbox 676, São Carlos, SP, 13565-905, Brazil

    Katia Maria Poloni & Ricardo José Ferrari

Authors
  1. Katia Maria Poloni
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  2. Ricardo José Ferrari
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Correspondence to Ricardo José Ferrari .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Poloni, K.M., Ferrari, R.J. (2018). Detection and Classification of Hippocampal Structural Changes in MR Images as a Biomarker for Alzheimer’s Disease. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-95162-1_28

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