Artificially enriching the training dataset of statistical shape models via constrained cage-based deformation

The construction of a powerful statistical shape model (SSM) requires a rich training dataset that includes the large variety of complex anatomical topologies. The lack of real data causes most SSMs unable to generalize possible unseen instances. Artificial enrichment of training data is one of the methods proposed to address this issue. In this paper, we introduce a novel technique called constrained cage-based deformation (CCBD), which has the ability to produce unlimited artificial data that promises to enrich variability within the training dataset. The proposed method is a two-step algorithm: in the first step, it moves a few handles together, and in the second step transfers the displacements of these handles to the base mesh vertices to generate a real new instance. The evaluation of statistical characteristics of the CCBD confirms that our proposed technique outperforms notable data-generating methods quantitatively, in terms of the generalization ability, and with respect to specificity.