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

Zhang, J, Zhong, Y, Smith, J and Gu, C 2017, 'ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation', Technology and Health Care, vol. 25, no. S1, pp. 231-239.


Document type: Journal Article
Collection: Journal Articles

Title ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation
Author(s) Zhang, J
Zhong, Y
Smith, J
Gu, C
Year 2017
Journal name Technology and Health Care
Volume number 25
Issue number S1
Start page 231
End page 239
Total pages 9
Publisher IOS Press
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 model-ing 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 con-nectivity of cells, through which the dynamic behaviors of soft tissue deformation are trans-formed 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 net-work to follow the principle of continuum mechanics to simulate soft tissue deformation.
Subject Biomechanical Engineering
Keyword(s) Surgical simulation
soft tissue deformation
cellular neural network
ChainMail method
real-time performance
DOI - identifier 10.3233/THC-171325
Copyright notice © 2017 IOS Press. Creative Commons Attribution Non-Commercial License 4.0
ISSN 0928-7329
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Altmetric details:
Access Statistics: 2 Abstract Views  -  Detailed Statistics
Created: Wed, 13 Sep 2017, 13:17:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us