Neuropathology holds the potential to advance our understanding of Alzheimer's disease and related dementias. Artificial intelligence (AI) models through well-labeled datasets can effectively perform complex tasks at or above human performance. Recent studies have analyzed neuropathological biomarkers like amyloid beta plaques and neurofibrillary tangles in whole slide images through AI models. However, appropriately labeled images are needed to detect which brain region the neuropathological biomarkers originate from for analytical purposes. The study aims to develop automated detection of gray matter regions in WSIs for downstream testing of established AI pipelines in neuropathology.The study involves manual annotations of gray matter regions on Bielschowsky silver stained WSIs from the Emory ADRC digital cohort. These annotations are used to train UNET semantic segmentation models for automated detection of gray matter regions on a larger cohort of non-annotated WSIs. Co-registration to immunohistochemically stained WSIs (amyloid beta, tau, TDP-43) is used to define gray matter annotations across stains. Established AI pipelines are then deployed in these regions to assess improvement on original results. 300 gray matter masks have been generated and an initial classifier has been trained using the YOLOv8 (You Only Look Once) model developed by Ultralytics. While the project is ongoing, our initial results have demonstrated the feasibility of developing an automated gray matter segmentation model to further understanding of biomarkers associated with clinical care.