A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. (1st November 2017)

• Record Type: Journal Article
• Authors: Martínez-Martínez, F.; Rupérez-Moreno, M.J.; Martínez-Sober, M.; Solves-Llorens, J.A.; Lorente, D.; Serrano-López, A.J.; Martínez-Sanchis, S.; Monserrat, C.; Martín-Guerrero, J.D.
• Is Part Of: Computers in biology and medicine. Volume 90(2017)
• Page Start: 116
• Page End: 124
• Publication Date: 2017-11-01
• Subjects: Breast biomechanics -- Finite element methods -- Machine learning -- Modeling -- Breast compression; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.09.019
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time ( < 0.2 s). Highlights: Machine Learning (ML) models were used to simulate in real-time the biomechanical behavior of the breast. Three ML models were trained with data from Finite Element (FE) simulations. Four experiments were designed to validate and study the models' performance. The mean error in the prediction of the nodal displacements was under 3 mm. The time needed for breast compression prediction was under 0.5 s.