Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques

Shunsuke Koga, Nikhil B. Ghayal, Dennis W. Dickson

Research output: Contribution to journalArticlepeer-review

Abstract

This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies.

Original languageEnglish (US)
Pages (from-to)306-312
Number of pages7
JournalJournal of Neuropathology and Experimental Neurology
Volume80
Issue number4
DOIs
StatePublished - Mar 22 2021

Keywords

  • Alzheimer disease
  • Corticobasal degeneration
  • Deep learning
  • Machine learning
  • Progressive supranuclear palsy

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Neurology
  • Clinical Neurology
  • Cellular and Molecular Neuroscience

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