A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

Tonutti Michele; Gras Gauthier; Yang Guang-Zhong

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A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

机译：用于图像引导神经外科手术中组织变形实时建模的机器学习方法

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Objectives: Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models.

机译：目标：实时准确地重建和可视化软组织变形在图像引导手术中至关重要，特别是在增强现实（AR）应用中。当前的变形模型的特征在于精度和计算速度之间的权衡。我们提出了一种方法，通过将预先计算的有限元方法（FEM）仿真结果与机器学习算法相结合，得出针对脑病理的特定于患者的变形模型。可以立即计算模型，并提供与FEM模型相当的精度。

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来源
《Artificial intelligence in medicine》 |2017年第7期|39-47|共9页
作者
Tonutti Michele; Gras Gauthier; Yang Guang-Zhong;
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作者单位

Imperial Coll London, Hamlyn Ctr Robot Surg, London SW7 2AZ, England;

Imperial Coll London, Hamlyn Ctr Robot Surg, London SW7 2AZ, England;

Imperial Coll London, Hamlyn Ctr Robot Surg, London SW7 2AZ, England;

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正文语种 eng
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关键词
Soft tissue deformation; Biomechanics; Finite element method; Image-guided surgery; Machine learning; Artificial neural networks; Support vector regression;

机译：软组织变形;生物力学;有限元方法;图像引导手术;机器学习;人工神经网络;支持向量回归;

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7. A Machine Learning Approach for Real-Time Modelling of Tissue Deformation in Image-Guided Neurosurgery [O] . Tonutti, MT, Gras, G, Yang, GZY 2017

机译：一种用于图像引导神经外科组织变形实时建模的机器学习方法

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

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