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Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging

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Deep Learning in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 171))

Abstract

Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.

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Notes

  1. 1.

    http://mipav.cit.nih.gov/index.php.

  2. 2.

    http://fsl.fmrib.ox.ac.uk/fsl/fslwiki.

  3. 3.

    https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Acknowledgements

This study was partly supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, and AG030514). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. 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, with details shown online.

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Authors and Affiliations

  1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Mingxia Liu, Chunfeng Lian & Dinggang Shen

Authors
  1. Mingxia Liu
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  2. Chunfeng Lian
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  3. Dinggang Shen
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Correspondence to Mingxia Liu .

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Editors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Yen-Wei Chen

  2. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, NSW, Australia

    Lakhmi C. Jain

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Liu, M., Lian, C., Shen, D. (2020). Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_8

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