A Brain Shift Correction Model Based On The Fuzzy Support Vector Machines (fsvm) With Different Constant Term

Brain shift contributes mostly to predictive error in Image-Guided Neurosurgery (IGNS). This study aims to build a statistical learning model between the quantity of the brain shift and the factors that impact on the brain shift, and predict the brain shift by using this model. The prediction of the brain shift can be considered as an estimation problem of regression function:the quantity of the brain shift is the output value of the function, while the corresponding factors that affect the brain shift can be taken as the input value of the function. This study is employing the FSVM with different constant term to build a brain shift correction model between the quantity of the brain shift and the factors that affect the brain shift. By taking the 10 clinical data sets and employing the novel predicting method, we trained the relational model of the multidimensional data for the brain tissue displacement, the direction of the surgical operation, and the operative site etc. The results indicate that the approach recaptured on 90% of the shift by validating the model with the leave-one-out method. This conducted research includes the component of multi-point shift. The result indicates that the correction model based on the FSVM with different constant term can be used to predict the brain shift with clinically acceptable accuracy. Therefore, the model can be applied to IGNS.