基于神经网络的生物组织松弛阶段力学建模
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1.南昌大学;2.南昌大学信息工程学院

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国家自然科学(81960327),江西省杰出青年人才(20171BCB23008)和自然科学基金(20192BAB207029)


Biomechanics modeling of tissues at relax stage based on Neural Network
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1.Nanchang University;2.School of Information Engineering,Nanchang University

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    摘要:

    目的 以猪肾为例,通过一系列对比、类比实验分析生物组织松弛阶段压应力变化影响因素,并建立一个较为准确且具有一定泛化性的生物组织松弛阶段力学模型。方法 利用自搭建力学实验平台,对猪肾实施不同境况下的压应力松弛实验。分析实验收集数据并整理作图,总结影响力变化的各种因素。基于获得结论采用神经网络学习算法,对猪肾松弛阶段力变化过程进行建模。结果 实验数据分析表明预挤压力、松弛时间为生物组织松弛阶段压应力变化的主要影响因素。模型验证实验结果显示:测试样本验证实验平均误差为0.0064N,泛化样本验证实验平均预测误差为0.0349N,建模效果良好。结论 神经网络建模算法具有泛化能力强、容错性好等优点,有利于为虚拟手术系统提供更为真实的力触觉反馈预测,对非线性生物组织力学建模而言是一种新思路。

    Abstract:

    Objective Taking pig kidney as an example, through a series of comparative and analogical experiments, this paper analyzes the influencing factors of the change of the pressure stress in the relaxation stage of biological tissue, and establishes a more accurate and widely applicable biomechanics model of the relaxation stage. Methods The stress relaxation experiments of pig kidney under different conditions were carried out by using the self built mechanical experiment platform. The collected data were analyzed and mapped, and various factors affecting the change of relaxation force were summarized. Based on the conclusion, the neural network learning algorithm is used to model the force change process in the relaxation stage of pig kidney. Results The analysis of the experimental data shows that the pre extrusion pressure and relaxation time are the main influencing factors of the stress change in the relaxation stage of biological tissue. The results of model validation experiment show that the average error of test sample validation experiment is 0.0064N, the average prediction error of generalization sample validation experiment is 0.0349N, so the modeling effect is good. Conclusions Neural network modeling algorithm has the advantages of strong generalization ability and good fault tolerance, which is conducive to provide more realistic force tactile feedback prediction for virtual surgery system. It is also a new idea for nonlinear biological tissue mechanics modeling.

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  • 收稿日期:2020-03-22
  • 最后修改日期:2020-04-22
  • 录用日期:2020-04-23
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