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Learning Longitudinal MRI Patterns by SICE and Deep Learning: Assessing the Alzheimer’s Disease Progression

  • Andrés OrtizEmail author
  • Jorge Munilla
  • Francisco J. Martínez-Murcia
  • Juan M. Górriz
  • Javier Ramírez
  • for the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 723)

Abstract

Automatic knowledge extraction from medical images constitutes a key point in the construction of computer aided diagnosis tools (CAD). This takes a special relevance in the case of neurodegenerative diseases such as the Alzheimer’s disease (AD), where an early diagnosis makes the treatments easier and more effective. Moreover, the study of the evolution of the illness results crucial to differentiate the neurodegenerative process associated to the disease from the natural degeneration due to the ageing process. In this paper we present a method to construct longitudinal models from subjects using a series of MRI images. Specifically, the method presented here aims to model Gray matter (GM) variation at different brain areas of a subject across subsequent examinations, being possible to relate those regions which degenerate jointly. Hence, it allows determining variation patterns that differentiate controls from AD patients. Additionally, White matter (WM) density is also incorporated to the longitudinal model to complement the information provided by GM. The results obtained demonstrated the effectiveness of the method in the extraction of these patterns, that can be used to classify between Controls (CN) and AD subjects with 94% of accuracy, outperforming other previous methods.

Notes

Acknowledgements

This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrés Ortiz
    • 1
    Email author
  • Jorge Munilla
    • 1
  • Francisco J. Martínez-Murcia
    • 2
  • Juan M. Górriz
    • 2
  • Javier Ramírez
    • 2
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Communications Engineering DepartmentUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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