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Deformation Expression of Soft Tissue Based on BP Neural Network

Xiaorui Zhang1,2,*, Xun Sun1, Wei Sun2, Tong Xu1, Pengpai Wang1, Sunil Kumar Jha3

1 Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Faculty of Information Technology, University of Information Technology and Management, Rzeszow, 35-225, Poland

* Corresponding Author: Xiaorui Zhang. Email:

Intelligent Automation & Soft Computing 2022, 32(2), 1041-1053.


This paper proposes a soft tissue grasping deformation model, where BP neural network optimized by the genetic algorithm is used to realize the real-time and accurate interaction of soft tissue grasping during virtual surgery. In the model, the soft tissue epidermis is divided into meshes, and the meshes generate displacements under the action of tension. The relationship between the tension and displacement of the mesh is determined by the proposed cylindrical spiral spring model. The optimized BP neural network is trained based on the sample data of the mesh point and vertical tension, so as to obtain the force and displacement of any mesh point on the soft tissue epidermis. The virtual experiment platform is built using a PHANTOM OMNI haptic hand controller and the 3D Max software, by which the simulation experiment of grasping the human abdomen is realized. The experimental results show that the proposed model has good visual interaction and real-time force feedback, which can meet the requirements of deformation simulation for soft tissue grasping in virtual surgery.


Cite This Article

X. Zhang, X. Sun, W. Sun, T. Xu, P. Wang et al., "Deformation expression of soft tissue based on bp neural network," Intelligent Automation & Soft Computing, vol. 32, no.2, pp. 1041–1053, 2022.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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