Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation.

Journal: NeuroImage
Published Date: Dec 20, 2021

Abstract

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.

Authors

  • Daniela Ushizima
    Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. dushizima@lbl.gov.
  • Yuheng Chen
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Maryana Alegro
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: maryana.alegro@ucsf.edu.
  • Dulce Ovando
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Rana Eser
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • WingHung Lee
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Kinson Poon
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Anubhav Shankar
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Namrata Kantamneni
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Shruti Satrawada
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Edson Amaro Junior
    University of Sao Paulo Medical School, Sao Paulo, Brazil.
  • Helmut Heinsen
    Medical School of the University of São Paulo, Av. Reboucas 381, São Paulo, SP 05401-000, Brazil. Electronic address: heinsen@mail.uni-wuerzburg.de.
  • Duygu Tosun
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
  • Lea T Grinberg
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: lea.grinberg@ucsf.edu.

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