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Automatic detection of amyloid beta plaques in somatosensory cortex of an Alzheimer’s disease mouse using deep learning

Citation

Yoon, H and Park, M and Yeom, S and Kirkcaldie, MTK and Summons, P and Lee, SH, Automatic detection of amyloid beta plaques in somatosensory cortex of an Alzheimer's disease mouse using deep learning, IEEE Access, 9 ISSN 2169-3536 (2021) [Refereed Article]


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Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1109/ACCESS.2021.3132401

Abstract

Identification of amyloid beta ( Aβ ) plaques in the cerebral cortex in models of Alzheimer’s Disease (AD) is of critical importance for research into therapeutics. Here we propose an innovative framework which automatically measures Aβ plaques in the cortex of a rodent model, based on anatomical segmentation using a deep learning approach. The framework has three phases: data acquisition to enhance image quality using preprocessing techniques and image normalization with a novel plaque removal algorithm, then an anatomical segmentation phase using the trained model, and finally an analysis phase to quantitate Aβ plaques. Supervised training with 946 sets of mouse brain section annotations exhibiting Aβ protein-labeled plaques ( Aβ plaques) were trained with deep neural networks (DNNs). Five DNN architectures: FCN32, FCN16, FCN8, SegNet, and U-Net, were tested. Of these, U-Net was selected as it showed the most reliable segmentation performance. The framework demonstrated an accuracy of 83.98% and 91.21% of the Dice coefficient score for atlas segmentation with the test dataset. The proposed framework automatically segmented the somatosensory cortex and calculated the intensity and extent of Aβ plaques. This study contributes to image analysis in the field of neuroscience, allowing region-specific quantitation of image features using a deep learning approach.

Item Details

Item Type:Refereed Article
Keywords:Alzheimer's disease, amyloid beta, brain atlas, deep learning, image segmentation
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Image processing
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Yoon, H (Mr Heemoon Yoon)
UTAS Author:Park, M (Dr Mira Park)
UTAS Author:Yeom, S (Dr Soonja Yeom)
UTAS Author:Kirkcaldie, MTK (Dr Matthew Kirkcaldie)
ID Code:149118
Year Published:2021
Deposited By:Information and Communication Technology
Deposited On:2022-03-10
Last Modified:2022-05-18
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