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Annual Conference on Medical Image Understanding and Analysis

MIUA 2017: Medical Image Understanding and Analysis pp 413–424Cite as

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

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Part of the book series: Communications in Computer and Information Science ((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.

Alzheimer’s Disease Neuroimaging Initiative—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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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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Authors and Affiliations

  1. Communications Engineering Department, University of Málaga, 29004, Málaga, Spain

    Andrés Ortiz & Jorge Munilla

  2. Department of Signal Theory, Communications and Networking, University of Granada, 18060, Granada, Spain

    Francisco J. Martínez-Murcia, Juan M. Górriz & Javier Ramírez

Authors
  1. Andrés Ortiz
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  2. Jorge Munilla
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  3. Francisco J. Martínez-Murcia
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  4. Juan M. Górriz
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  5. Javier Ramírez
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for the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Andrés Ortiz .

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Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

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Ortiz, A., Munilla, J., Martínez-Murcia, F.J., Górriz, J.M., Ramírez, J., for the Alzheimer’s Disease Neuroimaging Initiative. (2017). Learning Longitudinal MRI Patterns by SICE and Deep Learning: Assessing the Alzheimer’s Disease Progression. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_36

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  • DOI: https://doi.org/10.1007/978-3-319-60964-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

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