Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data

This paper proposes a spatiotemporal statistical shape model of a pediatric liver, which has potential applications in computer-aided diagnosis of the abdomen. Shapes are analyzed in the space of a level set function, which has computational advantages over the diffeomorphic framework commonly employed in conventional studies. We first calculate the time-varying average of the mean shape development using a kernel regression technique with adaptive bandwidth. Then, eigenshape modes for every timepoint are calculated using principal component analysis with an additional regularization term that ensures the smoothness of the temporal change of the eigenshape modes. To further improve the performance, we applied data augmentation using a level set-based nonlinear morphing technique. The proposed algorithm was evaluated in the context of a spatiotemporal statistical shape modeling of a liver using 42 manually segmented livers from children whose age ranged from approximately 2 weeks to 95 months. Our method achieved a higher generalization and specificity ability compared with conventional methods.