期刊
INTELLIGENT AUTOMATION AND SOFT COMPUTING
卷 32, 期 2, 页码 1041-1053出版社
TECH SCIENCE PRESS
DOI: 10.32604/iasc.2022.016543
关键词
Soft tissue grasping; genetic algorithm; BP neural network; surgical simulation
资金
- Natural Science Foundation of Jiangsu Province [BK20201136, BK20191401]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund
- NUIST Students' Platform for Innovation and Entrepreneurship Training Program
- NUIST Undergraduate Excellent Graduation Design (Thesis) Support Program Project
This paper proposes a soft tissue grasping deformation model using a BP neural network optimized by the genetic algorithm to achieve real-time and accurate interaction in virtual surgery. The experimental results show that the proposed model has good visual interaction and real-time force feedback.
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.
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