Why the study?
Surgical procedures for valvular diseases can be guided by patient-specific dynamic models providing information complementary to static 2D/3D images.
Does an automated model-free aortic valve segmentation method accurately segment the aortic valve compared to expert annotations in multislice CT images?
Does an automated model-free aortic valve segmentation method accurately segment the aortic valve compared to expert annotations in multislice CT images?
A novel automated model-free segmentation method for the aortic valve in CT images shows encouraging performance compared to expert annotations.
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 contrastenhanced 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.
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Ünay et al. (2021) studied this question.
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