Format

Send to

Choose Destination
Lab Invest. 2019 Feb 15. doi: 10.1038/s41374-019-0202-4. [Epub ahead of print]

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Signaevsky M1,2,3, Prastawa M1,4, Farrell K1,2,3, Tabish N1,2,3, Baldwin E1,2,3, Han N1,2,3, Iida MA1,2,3, Koll J1,4, Bryce C1,2,3, Purohit D1,2,5, Haroutunian V5,6, McKee AC7,8,9,10,11, Stein TD8,9,10,11, White CL 3rd12, Walker J12, Richardson TE12, Hanson R1,2,3, Donovan MJ1,4, Cordon-Cardo C1,4, Zeineh J1,4, Fernandez G1,4, Crary JF13,14,15.

Author information

1
Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
2
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
3
Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
4
Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA.
5
Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
6
J. James Peters VA Medical Center, Bronx, NY, USA.
7
Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA.
8
Department of Pathology, Boston University School of Medicine, Boston, MA, 02118, USA.
9
Alzheimer's Disease Center, CTE Program, Boston University School of Medicine, Boston, MA, 02118, USA.
10
Mental Illness Research, Education and Clinical Center, James J. Peters VA Boston Healthcare System, Boston, MA, 02130, USA.
11
Department of Veteran Affairs Medical Center, Bedford, MA, 01730, USA.
12
Neuropathology Laboratory, Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
13
Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. john.crary@mountsinai.org.
14
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. john.crary@mountsinai.org.
15
Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. john.crary@mountsinai.org.

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.

PMID:
30770886
DOI:
10.1038/s41374-019-0202-4

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center