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[A brain shift correction model based on the fuzzy support vector machines with different constant term].

Brain shift contributes mostly to the error of prediction in Image-Guided Neurosurgery (IGNS). In order to solve this problem, we build a statistical learning model between the quantity of the brain shift and we factors that impinge on the brain shift, and we predict the brain shift by using this model. The prediction of the brain shift can be regarded as an approach to estimation 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. In this study, we employ the fuzzy support vector machines (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 10 clinical data sets and employing the novel predicting method, we trained the relational model of the multi-dimensional data for the brain tissue displacement, the direction of surgical operation, and the operative site, etc. The results of validating the model by the leave-one-out method unveil that the approach recapitulated 90% of the shift, thus indicating 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.