Model-Free automatic segmentation of the aortic valve in multislice computed tomography images - Unay, Devrim; Harmankaya, Ibrahim; Oksuz, Ilkay; Cubuk, Rahmi; Celik, Levent; Kadipasaoglu, Kamuran (PAMUKKALE UNIV, 2021)
Valvular diseases may affect one or more of the cardiac valves, which may need to be replaced or restored for effective treatment. The surgical procedure can be guided by a patient-specific and dynamic model containing information complementary to the 2D/3D static images of the valves. To this end, in this study a novel automated model-free aortic valve segmentation method is presented, and its performance is evaluated against expert annotations over conventional contrast-enhanced ECG-gated multislice CT data of the aortic valve at its closed position. Detailed evaluation of the proposed method in 19 real cases revealed an encouraging performance of 3D region growing over Hessian based approach but also demonstrated the complexity of the problem.
Keywords: Aortic valve; Segmentation; Model-Free; Region growing; Hessian; Supravalvular sinus detection; Ascending aorta; Computed tomography; Valvular heart diseases; LEFT-VENTRICLE; CARDIAC CT; QUANTIFICATION; STENOSIS; Engineering