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A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery

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. Green open access

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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.

Type: Proceedings paper
Title: A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention
Location: Granada, Spain
Dates: 16th-20th September 2018
ISBN-13: 978-3-030-00936-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00937-3_28
Publisher version: https://doi.org/10.1007/978-3-030-00937-3_28
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Machine learning, Instrument bending, Neurosurgery, Trajectory prediction, Surgical planning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10060618
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