Sparse active shape models: Influence of the interpolation kernel on segmentation accuracy and speed

We analyze the segmentation of sparse data using the 3D variant of Active Shape Models by van Assen et al. (SPASM). This algorithm is designed to segment volumetric data represented by multiple planes with arbitrary orientations and with large undersampled regions. With the help of statistical shape constraints, the complicated interpolation of the sliced data is replaced by a mesh-based interpolation. To overcome large void areas without image information the mesh nodes are updated using a Gaussian kernel that propagates the available information to the void areas. Our analysis shows that the accuracy is mostly constant for a wide range of kernel scales, but the convergence speed is not. Experiments on simulated 3D echocardiography datasets indicate that an appropriate selection of the kernel can even double the convergence speed of the algorithm. Additionally, the optimal value for the kernel scale seems to be mainly related to the spatial frequency of the model encoding the statistical shape priors rather than to the sparsity of the sliced data. This suggests the possibility to precalculate the propagation coefficients which would reduce the computational load up to 40% depending on the spatial configuration of the input data.