Automated grey and white matter segmentation in digitized Aβ human brain tissue slide images

Zhengfeng Lai, Runlin Guo, Wenda Xu, Zin Hu, Kelsey Mifflin, Brittany N. Dugger, Chen Nee Chuah, Sen Ching Cheung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neuropathologists assess vast brain areas to identify diverse and subtly differentiated morphologies. Alzheimer's disease pathologies have different density distributions in grey matter (GM) and white matter (WM), making the task of separating GM and WM necessary to neuropathologic deep phenotyping. Standard methods of segmentation typically require manual annotations, where a trained observer traces the boundaries of GM and WM on digitized tissue slide images using software like Aperio ImageScope or QuPath. This method can be time-consuming and can prevent the analysis of large amounts of slides in a scalable way. In this paper, we propose a CNN-based approach to automatically segment GM and WM in ultra-high-resolution whole slide images (WSIs) by transforming the segmentation problem into a classification problem. Contrary to the traditional image processing segmentation method, our technique is flexible, robust, and efficient with the accuracy of 77.43% in GM and 79.42% in WM on our hold-out WSIs.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728114859
DOIs
StatePublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020 - London, United Kingdom
Duration: Jul 6 2020Jul 10 2020

Publication series

Name2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020

Conference

Conference2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
CountryUnited Kingdom
CityLondon
Period7/6/207/10/20

Keywords

  • Convolutional Neural Networks
  • Image Segmentation
  • Neuropathology
  • Ultra-high Resolution

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Media Technology

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