Prion diseases are a set of neurodegenerative conditions caused by prion protein (PrP). These neurodegenerative conditions are specifically caused by misfolded variations of the healthy PrP found in the brain, denoted as PrPC. Once a misfolded variant, denoted as PrPSC, is introduced to a healthy brain environment, the healthy variants are also misfolded; this inhibits cells from functioning properly. The PrPSc proteins aggregate in plaque formations, inhibiting neuron transmission and leading to decreased mobility, memory impairment, and eventually death. Previous studies have indicated a decrease in the density of specific neuronal subgroups known as GABAergic neurons. These previous studies scrutinized slices from specific regions of the brain, giving general insight into the decrease in cell density but not a precise quantitative change. Although they appear viable for the quantification task, neural networks are limited by their need for massive training sets. Random forest classifiers differ from neural networks as they use a set of predetermined values to make predictions; in this case, they account for pixel color, intensity, edge-ness, and texture to create an estimation of where cell bodies are located. The forest classifier program can be manually trained to improve its prediction of where cell bodies are located. The random forest classifier program is trained on healthy sets of mice brains but will then be utilized on scans from prion-affected mice. The study’s aim is to train the forest classifier program to generate an atlas that maps the impact of prion disease on GABAergic neurons responsible for releasing the neuro transmitter somatostatin.