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

Identification of amyloid beta ( <b>A</b><i>β</i> ) 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 <b>A</b><i>β</i> 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 <b>A</b><i>β</i> plaques. Supervised training with 946 sets of mouse brain section annotations exhibiting <b>A</b><i>β</i> protein-labeled plaques ( <b>A</b><i>β</i> 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 <b>A</b><i>β</i> plaques. This study contributes to image analysis in the field of neuroscience, allowing region-specific quantitation of image features using a deep learning approach.