TY - GEN
T1 - Automated grey and white matter segmentation in digitized Aβ human brain tissue slide images
AU - Lai, Zhengfeng
AU - Guo, Runlin
AU - Xu, Wenda
AU - Hu, Zin
AU - Mifflin, Kelsey
AU - Dugger, Brittany N.
AU - Chuah, Chen Nee
AU - Cheung, Sen Ching
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Convolutional Neural Networks
KW - Image Segmentation
KW - Neuropathology
KW - Ultra-high Resolution
UR - http://www.scopus.com/inward/record.url?scp=85091741916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091741916&partnerID=8YFLogxK
U2 - 10.1109/ICMEW46912.2020.9105974
DO - 10.1109/ICMEW46912.2020.9105974
M3 - Conference contribution
AN - SCOPUS:85091741916
T3 - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
BT - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
Y2 - 6 July 2020 through 10 July 2020
ER -