Automatic Aortic Root Segmentation with Shape Constraints and Mesh Regularisation

Fully automated 3D segmentation is not only challenging due to, for instance, ambiguities in appearance, but it is also computationally demanding. We present a fullyautomatic, learning-based deformable modelling method for segmenting the aortic root in CT images using a two-stage mesh deformation: a non-iterative boundary segmentation with a statistical shape model for shape constraint, followed by an iterative boundary refinement process. At both stages, we introduce a B-spline mesh regularisation technique to avoid mesh entanglement during deformation. The initialisation of the deformable model is achieved through efficient detection and localisation of the aortic root using marginal space learning, which carries out similarity parameter estimation in an incremental fashion. Quantitative comparisons are carried out against a state-of-the-art deformable model-based approach and an active shape model based segmentation. The proposed method achieves both a lower average mesh error of 1.39±0.29mm, and Hausdorff distance of 6.75±2.05mm. Compared to these two approaches, it results in much more regularised mesh surfaces with no tangled mesh faces.