Abstract

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.

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Acknowledgments

The authors would like to acknowledge NIH grants R01AG054008 (JFC), R01NS095252 (JFC), R01AG062348 (ACM/JFC), RF1AG060961 (JFC), F32AG056098 (KF), Department of Defense W81XWH-13-MRPRA-CSRA, the Tau Consortium (Rainwater Charitable Trust), and the Alzheimer’s Association (NIRG-15-363188). The first author was also supported by a Career Development Award funded by NIH-NOA 3P50AG005138 (MS). We thank Jill Gregory for the illustration. The authors also would like to thank Ping Shang, HT, Jeff Harris, HTL, and Chan Foong, MS, for technical assistance, and Javed and Shahnaz Iqbal Family Trust for the generous donation.

Author information

Affiliations

  1. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Maxim Signaevsky
    • , Marcel Prastawa
    • , Kurt Farrell
    • , Nabil Tabish
    • , Elena Baldwin
    • , Natalia Han
    • , Megan A. Iida
    • , John Koll
    • , Clare Bryce
    • , Dushyant Purohit
    • , Russell Hanson
    • , Michael J. Donovan
    • , Carlos Cordon-Cardo
    • , Jack Zeineh
    • , Gerardo Fernandez
    •  & John F. Crary
  2. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Maxim Signaevsky
    • , Kurt Farrell
    • , Nabil Tabish
    • , Elena Baldwin
    • , Natalia Han
    • , Megan A. Iida
    • , Clare Bryce
    • , Dushyant Purohit
    • , Russell Hanson
    •  & John F. Crary
  3. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Maxim Signaevsky
    • , Kurt Farrell
    • , Nabil Tabish
    • , Elena Baldwin
    • , Natalia Han
    • , Megan A. Iida
    • , Clare Bryce
    • , Russell Hanson
    •  & John F. Crary
  4. Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA

    • Marcel Prastawa
    • , John Koll
    • , Michael J. Donovan
    • , Carlos Cordon-Cardo
    • , Jack Zeineh
    •  & Gerardo Fernandez
  5. Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    • Dushyant Purohit
    •  & Vahram Haroutunian
  6. J. James Peters VA Medical Center, Bronx, NY, USA

    • Vahram Haroutunian
  7. Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA

    • Ann C. McKee
  8. Department of Pathology, Boston University School of Medicine, Boston, MA, 02118, USA

    • Ann C. McKee
    •  & Thor D. Stein
  9. Alzheimer’s Disease Center, CTE Program, Boston University School of Medicine, Boston, MA, 02118, USA

    • Ann C. McKee
    •  & Thor D. Stein
  10. Mental Illness Research, Education and Clinical Center, James J. Peters VA Boston Healthcare System, Boston, MA, 02130, USA

    • Ann C. McKee
    •  & Thor D. Stein
  11. Department of Veteran Affairs Medical Center, Bedford, MA, 01730, USA

    • Ann C. McKee
    •  & Thor D. Stein
  12. Neuropathology Laboratory, Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA

    • Charles L. White III
    • , Jamie Walker
    •  & Timothy E. Richardson

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Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to John F. Crary.

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DOI

https://doi.org/10.1038/s41374-019-0202-4