Real-time nonlinear FEM with neural network for simulating soft organ model deformation

Ken'Ichi Morooka, Xian Chen, Ryo Kurazume, Seiichi Uchida, Kenji Hara, Yumi Iwashita, Makoto Hashizume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. [2] that a deformed model can be estimated from the superposition of basic deformation modes. The neural network finds a relationship between external forces and the models deformed by the forces. The experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear FEM computation.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages742-749
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2008
Event11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States
Duration: Sep 6 2008Sep 10 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5242 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
CountryUnited States
CityNew York, NY
Period9/6/089/10/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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