A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning
作者：
Highlights：
• Machine learning (ML) to model the liver biomechanical behaviour during breathing.
• ML is much faster than the popular FEM, allowing real-time soft tissue modelling.
• Modelling scheme able to predict deformation for a new load and a new liver.
• ML regression models were used: three tree-based methods and two simpler ones.
• Good prediction performance was obtained: all samples with an error under 1 mm.
摘要
•Machine learning (ML) to model the liver biomechanical behaviour during breathing.•ML is much faster than the popular FEM, allowing real-time soft tissue modelling.•Modelling scheme able to predict deformation for a new load and a new liver.•ML regression models were used: three tree-based methods and two simpler ones.•Good prediction performance was obtained: all samples with an error under 1 mm.
论文关键词：Soft tissue deformation,Biomechanical behaviour,Liver,Machine learning,Tree-based regression
论文评审过程：Received 26 July 2016, Revised 11 November 2016, Accepted 26 November 2016, Available online 28 November 2016, Version of Record 9 December 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.11.037
