Echocardiography is the leading imaging modality for cardiac disorders in clinical practice. During an echocardiographic exam, geometry and blood flow are quantified in order to assess cardiac function. In clinical practice, these imagebased measurements are currently performed manually. An automated approach is needed if more advanced analysis is desired. In this article, we propose a new hybrid framework for the construction of a disease-specific atlas to improve Doppler aortic outflow velocity profile segmentation. The proposed method is based on combining realistic computational simulations of the cardiovascular system for common cardiac conditions (using CircAdapt) with a validated image-based atlas construction method. The coupling is realized via model-based generation of echocardiographic images of virtual populations with a statistically approved parameter variation. We created virtual populations of 100 healthy individuals and 100 patients with aortic stenosis, synthesized their aortic Doppler velocity images and constructed the corresponding atlases. We validated atlases’ performances by comparing their segmentation of real clinical images with the manually segmented ground truth. The experimental results show that the segmentation accuracy obtained using the proposed atlases is comparable to the accuracy obtained using classical clinical image-based atlases. Moreover, this framework eliminates the time-consuming acquisition of a sufficient number of representative images in clinical practice, offering a substantial time efficiency and flexibility in creating a disease specific atlas and ensuring an observer-independent automated segmentation. The proposed approach can easily be extended towards the creation of atlases for segmenting any Doppler trace in the cardiovascular circulation in a specific disease.

Research leading to these results has received funding from the Ministry of Science, Education and Sports, Republic of Croatia (036-0362214-1989), Subprograma de Proyectos de Investigacin en Salud (FIS), Instituto de Salud Carlos III, Spain (ref. PI11/01709); the Spanish Ministry of Economy and Competitiveness (grant TIN2014-52923-R) and FEDER and the Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 611823. Additionally, we would like to thank Frank Weidemann for a subset of the patient images, and Catalina Tobon-Gomez and Georgina Palau-Caballero for provided CircAdapt support.