Deep Learning Based Large-scale Histological Tau Protein Mapping for Neuroimaging Biomarker Validation in Alzheimer’s Disease

Deposits of abnormal tau protein inclusions in the brain are one of the pathological hallmarks of Alzheimer’s disease (AD) and are the best predictor of neuronal loss and clinical decline. As such, imaging-based biomarkers to detect tau deposits in-vivo could leverage AD diagnosis and monitoring from earlier disease stages. Although several PET-based tau tracers are available for research studies, validation of such tracers against direct detection of tau deposits in brain tissue remain unresolved. Large-scale voxel-to-voxel correlation has been challenging because of the size of the human brain, deformation caused by tissue processing that precludes registration and the amount of tau inclusion. In this study, we created a semi-automated computer vision pipeline for segmenting tau inclusions in billion-pixel digital pathology images of whole human brains, aiming at generating quantitative, tridimensional tau density maps that can be used deciphering the distribution of tau inclusions along AD progression and validate PET tau tracers. Our pipeline comprises several pre and post-processing steps developed to handle the high complexity of these brain digital pathology images. SlideNet, a convolutional neural network designed to work with our large-datasets to locate and segment tau inclusions is the heart of the pipeline. Using our novel method, we have successfully processed >500 slides from two whole human brains, immunostained for two phospho-tau antibodies (AT100 and AT8) spanning several Gigabytes of images. Our network obtained strong tau inclusion segmentation results with ROC AUC of 0.89 and 0.85 for AT100 and AT8, respectively. Introspection studies further assessed the ability of our trained model to lean tau-related features. As the final results, our pipeline successfully created 3d tau density maps that were co-registered to the histology 3d maps.