ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation

Jinao Zhang, Yongmin Zhong, Julian Smith, Chengfan Gu

Research output: Contribution to journalConference articleResearchpeer-review

7 Citations (Scopus)

Abstract

Background: Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. Objective: In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. METHOD: The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Results: Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. Conclusions: The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Original languageEnglish
Pages (from-to)S231-S239
Number of pages9
JournalTechnology and Health Care
Volume25
Issue numberS1
DOIs
Publication statusPublished - 21 Jul 2017
EventInternational Conference on Biomedical Engineering and Biotechnology (iCBEB) 2016 - Hangzhou, China
Duration: 1 Aug 20164 Aug 2016
Conference number: 5th

Keywords

  • Cellular neural network
  • ChainMail method
  • Real-time performance
  • Soft tissue deformation
  • Surgical simulation

Cite this

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title = "ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation",
abstract = "Background: Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. Objective: In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. METHOD: The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Results: Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. Conclusions: The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.",
keywords = "Cellular neural network, ChainMail method, Real-time performance, Soft tissue deformation, Surgical simulation",
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ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation. / Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan.

In: Technology and Health Care, Vol. 25, No. S1, 21.07.2017, p. S231-S239.

Research output: Contribution to journalConference articleResearchpeer-review

TY - JOUR

T1 - ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation

AU - Zhang, Jinao

AU - Zhong, Yongmin

AU - Smith, Julian

AU - Gu, Chengfan

PY - 2017/7/21

Y1 - 2017/7/21

N2 - Background: Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. Objective: In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. METHOD: The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Results: Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. Conclusions: The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

AB - Background: Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. Objective: In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. METHOD: The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Results: Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. Conclusions: The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

KW - Cellular neural network

KW - ChainMail method

KW - Real-time performance

KW - Soft tissue deformation

KW - Surgical simulation

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