Aortic dissection is a condition in which a tear in the inner wall of the aorta allows blood to flow between two layers of the aortic wall. Aortic dissection is associated with severe chest pain and can be deadly. Contrast-enhanced CT is the main modality for detection of aortic dissection. Aortic dissection is one of the target abnormalities during evaluation of a triple rule-out CT in emergency cases. In this paper, we present a method for automatic patient-level detection of aortic dissection. Our algorithm starts by an atlas-based segmentation of the aorta which is used to produce cross-sectional images of the organ. Segmentation refinement, flap detection and shape analysis are employed to detect aortic dissection in these cross-sectional slices. Then, the slice-level results are aggregated to render a patient-level detection result. We tested our algorithm on a data set of 37 contrast-enhanced CT volumes, with 13 cases of aortic dissection. We achieved an accuracy of 83.8%, a sensitivity of 84.6% and a specificity of 83.3%.