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

Abstract

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.

Keywords

• Alzheimer’s disease
• amyloid beta
• brain atlas
• deep learning
• image segmentation