Hyperbolic space sparse coding with its application on prediction of Alzheimer’s disease in mild cognitive impairment

Jie Zhang, Jie Shi, Cynthia M Stonnington, Qingyang Li, Boris A. Gutman, Kewei Chen, Eric M. Reiman, Richard John Caselli, Paul M. Thompson, Jieping Ye, Yalin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Mild Cognitive Impairment (MCI) is a transitional stage between normal age-related cognitive decline and Alzheimer’s disease (AD). Here we introduce a hyperbolic space sparse coding method to predict impending decline of MCI patients to dementia using surface measures of ventricular enlargement. First,we compute diffeomorphic mappings between ventricular surfaces using a canonical hyperbolic parameter space with consistent boundary conditions and surface tensorbased morphometry is computed to measure local surface deformations. Second,ring-shaped patches of TBM features are selected according to the geometric structure of the hyperbolic parameter space to initialize a dictionary. Sparse coding is then applied on the patch features to learn sparse codes and update the dictionary. Finally,we adopt max-pooling to reduce the feature dimensions and apply Adaboost to predict AD in MCI patients (N = 133) from the Alzheimer’s Disease Neuroimaging Initiative baseline dataset. Our work achieved an accuracy rate of 96.7% and outperformed some other morphometry measures. The hyperbolic space sparse coding method may offer a more sensitive tool to study AD and its early symptom.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages326-334
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
StatePublished - 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 21 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period10/21/1610/21/16

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Keywords

  • Hyperbolic parameter space
  • Mild Cognitive Impairment
  • Ring-shaped patches
  • Sparse coding and dictionary learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, J., Shi, J., Stonnington, C. M., Li, Q., Gutman, B. A., Chen, K., Reiman, E. M., Caselli, R. J., Thompson, P. M., Ye, J., & Wang, Y. (2016). Hyperbolic space sparse coding with its application on prediction of Alzheimer’s disease in mild cognitive impairment. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 326-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_38