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Transcatheter aortic valve implantation (TAVI) is becoming a standard treatment for non-operable and high-risk patients with symptomatic severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, comprehensive preoperative planning is crucial for a successful outcome, with the most important decisions made during planning being the selection of the proper implant size, and determining the correct C-arm angulations. While geometric models extracted from 3D images are often used to derive these measurements, the complex shape variation of the AV anatomy found in these patients causes many of the shape representations used to estimate such geometric models to fail in capturing morphological characteristics in sufficient detail. In addition, most current approaches only model the aortic valve (AV), omitting modeling the left ventricle outflow tract (LVOT) entirely despite its high correlation with severe complications such as annulus ruptures, paravalvular leaks and myocardial infarction. We propose a fully automated method to extract patient specific models of the AV and the LVOT, and derive comprehensive biomarkers for accurate TAVI planning. We utilize a novel shape representation -the ShapeForest -which is able to model complex shape variation, preserves local shape information, and incorporates prior knowledge during shape space inference. Extensive quantitative and qualitative experiments performed on 630 volumetric data sets demonstrate an accuracy of 0.69 mm for the AV and 0.83 mm for the LVOT, an improvement of over 16% and 18% respectively when compared against state of the art methods.

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