Automatic Detection of Amyloid Beta Plaques in Somatosensory Cortex of an Alzheimer's Disease Mouse Using Deep Learning

Identification of amyloid beta ( \text{A}\beta ) 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 \text{A}\beta plaques in the cortex of a rodent...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.161926-161936
Main Authors: Yoon, Heemoon, Park, Mira, Yeom, Soonja, Kirkcaldie, Matthew T. K., Summons, Peter, Lee, Sang-Hee
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
amyloid beta
Annotations
Artificial neural networks
Brain
brain atlas
Brain modeling
Cerebral cortex
Deep learning
Image acquisition
Image analysis
Image enhancement
Image quality
Image segmentation
Machine learning
Magnetic resonance imaging
Medical imaging
Mice
Pathology
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Summary:Identification of amyloid beta ( \text{A}\beta ) 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 \text{A}\beta 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 \text{A}\beta plaques. Supervised training with 946 sets of mouse brain section annotations exhibiting \text{A}\beta protein-labeled plaques ( \text{A}\beta 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 \text{A}\beta plaques. This study contributes to image analysis in the field of neuroscience, allowing region-specific quantitation of image features using a deep learning approach.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3132401