Part-based localisation and segmentation of landmark-related auditory cortical regions

Abstract

We recently presented a method for the delineation of cortical regions of interest that relies on the finite element decomposition of shape [21]. Our current work strengthens and extends the proposed technique with the following contributions: First, we provide a detailed discussion of the computational challenges related to applying the hierarchical shape modelling and energy minimisation approach to the representation and segmentation of specific areas in cortical surfaces. Second, we analyse the underlying heuristics in order to elucidate the representational power and accuracy of the a priori constrained, partial model of the auditory cortex anatomy, and improve the cortical landmark localisation. We show experimentally that a valid parametric prior can be built from expert prior knowledge in a straightforward manner. By employing the advantages of the hierarchical shape decomposition, the model can be substantially improved on the basis of training sets, which are much smaller compared with state-of-the-art methods.