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

  • Atsushi SaitoEmail author
  • Koyo Nakayama
  • Antonio R. Porras
  • Awais Mansoor
  • Elijah Biggs
  • Marius George Linguraru
  • Akinobu Shimizu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


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.


Spatiotemporal 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.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Health SystemWashington, D.C.USA
  3. 3.School of Medicine and Health SciencesGeorge Washington UniversityWashington, D.C.USA

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