Convolutional Neural Networks for the Identification of Regions of Interest in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer’s Disease

  • Andreas Karwath
  • Markus Hubrich
  • Stefan KramerEmail author
  • the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)


When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.


PET Scans Alzheimer’s disease Dementia Deep learning Convolutional Neural Networks Regions of interest Representation learning 



This work was partially supported by the Carl-Zeiss-Foundation Competence Center for High Performance Computing.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( 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:


  1. 1.
    Li, R., Perneczky, R., Drzezga, A., Kramer, S.: Gaussian mixture models and model selection for [18F] fluorodeoxyglucose positron emission tomography classification in Alzheimer’s disease. PLoS ONE 10(4), e0122731 (2015)CrossRefGoogle Scholar
  2. 2.
    Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci. Biobehav. Rev. 74, (Part A), 58–75 (2017)Google Scholar
  3. 3.
    Jagust, W.J., Landau, S.M., Koeppe, R.A., Reiman, E.M., Chen, K., Mathis, C.A., Price, J.C., Foster, N.L., Wang, A.Y.: The ADNI PET core: 2015. Alzheimers Dement. 11(7), 757–771 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut für InformatikJohannes Gutenberg-UniversitätMainzGermany

Personalised recommendations