A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning

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• 作者：D. Lorente ; ^a ; ^{delia.lorente@uv.es} ; F. Martí ; nez-Martí ; nez^a ; ^{francisco.martinez-martinez@uv.es} ; M.J. Rupé ; rez^b ; ^{mjrupere@upvnet.upv.es} ; M.A. Lago^c ; ^{Miguel.Lago@psych.ucsb.edu} ; M. Martí ; nez-Sober^a ; ^{marcelino.martinez@uv.es} ; P. Escandell-Montero^a ; ^{pablo.escandell@uv.es} ; J.M. Martí ; nez-Martí ; nez^a ; ^{jose.maria.martinez@uv.es} ; S. Martí ; nez-Sanchis^b ; ^{sanmars1@upv.es} ; A.J. Serrano-Ló ; pez^a ; ^{antonio.j.serrano@uv.es} ; C. Monserrat^c ; ^{cmonserr@dsic.upv.es} ; J.D. Martí ; n-Guerrero^a ; ^{jose.d.martin@uv.es}
• 关键词：Soft tissue deformation ; Biomechanical behaviour ; Liver ; Machine learning ; Tree-based regression
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：1 April 2017
• 年：2017
• 卷：71
• 期：Complete
• 页码：342-357
• 全文大小：3270 K
• 卷排序：71

文摘

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.