Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue

José David Martín-Guerrero, María J. Rupérez-Moreno, Francisco Martínez-Martínez, Delia Lorente-Garrido, Antonio J. Serrano-López, Carlos Monserrat, Sandra Martínez-Sanchis, Marcelino Martínez-Sober

Abstract: An accurate modeling of the biomechanical properties of human soft tissue is crucial in many clinical applications, such as, radiotherapy administration or surgery. The finite element method (FEM) is the usual choice to carry out such modeling due to its high accuracy. However, FEM is computationally very costly, and hence, its application in real-time or even off-line with short delays are still challenges to overcome. This paper proposes a framework based on Machine Learning to learn FEM modeling, thus having a tool able to yield results that may be sufficiently fast for clinical applications. In particular, the use of ensembles of Decision Trees has shown its suitability in modeling the behavior of the liver and the breast.