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.
KeywordsSpatiotemporal analysis Statistical shape model Pediatric Liver
This work is partly supported by KAKENHI (No. 26108002, 16H06785 and 18H03255) and the Sheikh Zayed Institute at Children’s National Health System.
- 5.Mansi, T., et al.: A statistical model of right ventricle in tetralogy of fallot for prediction of remodelling and therapy planning. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 214–221. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04268-3_27CrossRefGoogle Scholar
- 8.Alam, S., Kobashi, S., Nakano, R., Morimoto, M., Aikawa1, S., Shimizu, A.: Spatiotemporal statistical shape model construction for longitudinal brain deformation analysis using weighted PCA. In: Computer Assisted Radiology and Surgery (CARS) 2016, vol. 11, p. S204 (2016)Google Scholar
- 11.Saito, A., Nakada, M., Oost, E., Shimizu, A., Watanabe, H., Nawano, S.: A statistical shape model for multiple organs based on synthesized-based learning. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds.) ABD-MICCAI 2013. LNCS, vol. 8198, pp. 280–289. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41083-3_31CrossRefGoogle Scholar
- 13.Uchida, Y., Shimizu, A., Kobatake, H., Nawano, S., Shinozaki, K.: A comparative study of statistical shape models of the pancreas. In: Computer Assisted Radiology and Surgery (CARS) 2010, vol. 5, pp. S385–S387 (2010)Google Scholar