Atlas construction for cardiac velocity profiles segmentation using a lumped computational model of circulatory system

Heart diseases are a leading cause of death worldwide, making a prompt and accurate diagnosis of cardiac functionality an important task. Recordings of cardiac outflow Doppler velocity profiles, obtained during an echocardiographic examination, are important to quantify hemodynamics and infer cardiac function. For automated segmentation and quantification of these images, a statistical atlas based approach has been proposed previously. Since acquiring a sufficient amount of data for an atlas can be a slow process in clinical practice and possibly result in a small and/or not representative dataset, we present an alternative approach for construction of the statistical atlas. This approach is based on simulating data from virtual patients, using a lumped computational model (CircAdapt), which incorporates knowledge of physiological processes in the human circulatory system under both normal and pathological conditions.