Deformation estimation of an elastic object by partial observation using a neural network

Utako Yamamoto, Megumi Nakao, Masayuki Ohzeki, Tetsuya Matsuda

Research output: Contribution to journalArticlepeer-review


Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural network was introduced to estimate the entire deformation of an object. We applied our method to two elastic objects; a rectangular parallelepiped model, and a human liver model reconstructed from computed tomography data. The average estimation error for the human liver model was 0.041 mm when the object was deformed up to 66.4 mm, from only around 3 % observations. These results indicate that the deformation of an entire elastic object can be estimated with an acceptable level of error from limited observations by applying a trained neural network to a new deformation.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2017 Nov 28


  • Deformation estimation
  • Elastic object
  • Liver
  • Multi-layer neural network
  • Nonlinear finite element method
  • Partial observation

ASJC Scopus subject areas

  • General

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