Computed Tomography (CT) is a key tool for COVID-19 pneumonia staging due to the possibility to quantify relevant imaging findings such as Ground Glass Opacity (GGO) and consolidation. While automatic lung and COVID-19 infection segmentation has been successfully tackled using Deep Learning models, for the infection classification into these imaging findings, the use of fixed thresholds remains the preferred method in literature. However, this method does not consider the evolutionary pathological processes involved in the disease. We perform automatic segmentation of lung and COVID-19 infection through a 3D-UNet and propose the use of Gaussian Mixture Models (GMM) to characterize the GGO and consolidation on each CT scan from a probabilistic perspective. The segmentation of lung and COVID-19 infection achieved a Dice Similarity Coefficient of 0.973±0.015 and 0.817±0.119 (mean±SD), respectively. Using the probability distributions obtained through GMM a dynamic decision boundary was defined for each CT for GGO and consolidation voxel-wise classification. Visual comparison of the use of dynamic and fixed thresholds was performed by 3 experts, revealing similar results for most of the studied CT scans. Currently, the clinical validation of the model is in progress.