Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning.

ABSTRACT

PURPOSE  Electrode bending observed after stereotactic interventions is typically not accounted for in either computer-assisted planning algorithms, where straight trajectories are assumed, or in quality assessment, where only metrics related to entry and target points are reported. Our aim is to provide a fully automated and validated pipeline for the prediction of stereo-electroencephalography (SEEG) electrode bending. METHODS  We transform electrodes of 86 cases into a common space and compare features-based and image-based neural networks on their ability to regress local displacement ([Formula see text]) or electrode bending ([Formula see text]). Electrodes were stratified into six groups based on brain structures at the entry and target point. Models, both with and without Monte Carlo (MC) dropout, were trained and validated using tenfold cross-validation. RESULTS  mage-based models outperformed features-based models for all groups, and models that predicted [Formula see text] performed better than for [Formula see text]. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% ([Formula see text]) and 39.9% ([Formula see text]), compared to no dropout. Using an image of brain tissue types (cortex, white and deep grey matter) resulted in similar, and sometimes better performance, compared to using a T1-weighted MRI when predicting [Formula see text]. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE[Formula see text] mm. CONCLUSION  An image-based approach regressing local displacement with an image of brain tissue types resulted in more accurate electrode bending predictions compared to other approaches, inputs, and outputs. Future work will investigate the integration of electrode bending into planning and quality assessment algorithms.