Granados, A;
Mancini, M;
Vos, SB;
Lucena, O;
Vakharia, V;
Rodionov, R;
Miserocchi, A;
... Ourselin, S; + view all
(2018)
A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery.
In: Frangi, AF and Schnabel, JA and Davatzikos, Ch and Alberola-López, C and Fichtinger, G, (eds.)
Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention 2018.
(pp. pp. 238-246).
Springer Nature: Cham, Switzerland.
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Abstract
The accurate implantation of stereo-electroencephalography (SEEG) electrodes is crucial for localising the seizure onset zone in patients with refractory epilepsy. Electrode placement may differ from planning due to instrument deflection during surgical insertion. We present a regression-based model to predict instrument bending using image features extracted from structural and diffusion images. We compare three machine learning approaches: Random Forest, Feed-Forward Neural Network and Long Short-Term Memory on accuracy in predicting global instrument bending in the context of SEEG implantation. We segment electrodes from post-implantation CT scans and interpolate position at 1 mm intervals along the trajectory. Electrodes are modelled as elastic rods to quantify 3 degree-of-freedom (DOF) bending using Darboux vectors. We train our models to predict instrument bending from image features. We then iteratively infer instrument positions from the predicted bending. In 32 SEEG post-implantation cases we were able to predict trajectory position with a MAE of 0.49 mm using RF. Comparatively a FFNN had MAE of 0.71 mm and LSTM had a MAE of 0.93 mm.
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