Misdiagnosis of community-acquired pneumonia is an important clinical problem, leading to a high rate of mortality. Diagnoses are typically conducted using two-dimensional chest x-rays, which have shown to be time-consuming and inaccurate. In an effort to improve the current diagnostic method, we utilized Micro-Computed Tomography (MicroCT) and image analysis software to develop a diagnostic algorithm that can quantitatively assess the severity of pneumonia in mice. We believed this method would provide more immediate, precise, and accurate diagnoses as opposed to the qualitative assessments done by radiologists at present, because MicroCT provides opportunities for non-invasive radiographic endpoints for pneumonia studies. A quantitative scoring of previously obtained Computed Tomography (CT) scans of pneumonia infected and control mice lungs was developed with a semi-automated image segmentation algorithm. At the endpoint of 168 hours, each of the mice was categorized as either a) a Saline (control)-injected mouse (total=13), a Pneumonia-injected Survivor (total=11), or a Pneumonia- injected Non-survivor (total=11). Three comparison tests were then completed, including Saline vs. All Pneumonia Injected Mice, Pneumonia Survivors vs. Pneumonia Non-survivors, and All Survivors (both Saline & Pneumonia) vs. Pneumonia Non-survivors. In all three comparisons, the semi-automated algorithm was better able to distinguish between the different groups than radiologists using two-dimensional chest x-rays of the mice’s lungs, with p-values of 0.001, 0.039, and 0.001 for the semi-automated algorithm, and 0.004, 0.581, 0.058 for the radiologists, respectively.