Real‐time biomechanical modelling of the liver using LightGBM model. Issue 6 (27th June 2022)

• Record Type: Journal Article
• Authors: Zhu, Jiahua; Su, Yixian; Liu, Ziteng; Liu, Bainan; Sun, Yu; Gao, Wenpeng; Fu, Yili
• Is Part Of: International journal of medical robotics and computer assisted surgery. Volume 18:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-27
• Subjects: biomechanical liver modelling -- finite element method -- the LightGBM regression model; Robotics in medicine -- Periodicals; Surgery -- Technological innovations -- Periodicals; Imaging systems in medicine -- Periodicals; 617.90285
• DOI: 10.1002/rcs.2433
• Languages: English
• ISSNs: 1478-5951

Abstract

Abstract: Background: Accurate and real‐time biomechanical modelling of the liver is a major challenge in computer‐assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations. Method: A liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5 N in omni‐direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree‐based LightGBM regression model trained with the generated data set. Results: In comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774 mm, 0.0786 mm, 0.0801 mm in the mean absolute error (MAE) and 0.0591 mm, 0.0609 and 0.0622 mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33 ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91 s). Conclusion: These results lay a foundation for the future development of real‐time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.