A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. (1st April 2017)

• Record Type: Journal Article
• Authors: Lorente, D.; Martínez-Martínez, F.; Rupérez, M.J.; Lago, M.A.; Martínez-Sober, M.; Escandell-Montero, P.; Martínez-Martínez, J.M.; Martínez-Sanchis, S.; Serrano-López, A.J.; Monserrat, C.; Martín-Guerrero, J.D.
• Is Part Of: Expert systems with applications. Volume 71(2017)
• Page Start: 342
• Page End: 357
• Publication Date: 2017-04-01
• Subjects: Soft tissue deformation -- Biomechanical behaviour -- Liver -- Machine learning -- Tree-based regression; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.11.037
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions.