Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery

TY - CHAP

T1 - Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery

AU - Granados, Alejandro

AU - Lucena, Oeslle

AU - Vakharia, Vejay

AU - Miserocchi, Anna

AU - McEvoy, Andrew W.

AU - Vos, Sjoerd B.

AU - Rodionov, Roman

AU - Duncan, John S.

AU - Sparks, Rachel

AU - Ourselin, Sebastien

PY - 2020/4

Y1 - 2020/4

N2 - Implantation accuracy of electrodes during stereotactic neurosurgery is necessary to ensure safety and efficacy. However, electrodes deflect from planned trajectories. Although mechanical models and data-driven approaches have been proposed for trajectory prediction, they lack to report uncertainty of the predictions. We propose to use Monte Carlo (MC) dropout on neural networks to quantify uncertainty of predicted electrode local displacement. We compute image features of 23 stereoelectroencephalography cases (241 electrodes) and use them as inputs to a neural network to regress electrode local displacement. We use MC dropout with 200 stochastic passes to quantify uncertainty of predictions. To validate our approach, we define a baseline model without dropout and compare it to a stochastic model using 10-fold cross-validation. Given a starting planned trajectory, we predicted electrode bending using inferred local displacement at the tip via simulation. We found MC dropout performed better than a non-stochastic baseline model and provided confidence intervals along the predicted trajectory of electrodes. We believe this approach facilitates better decision making for electrode bending prediction in surgical planning.

AB - Implantation accuracy of electrodes during stereotactic neurosurgery is necessary to ensure safety and efficacy. However, electrodes deflect from planned trajectories. Although mechanical models and data-driven approaches have been proposed for trajectory prediction, they lack to report uncertainty of the predictions. We propose to use Monte Carlo (MC) dropout on neural networks to quantify uncertainty of predicted electrode local displacement. We compute image features of 23 stereoelectroencephalography cases (241 electrodes) and use them as inputs to a neural network to regress electrode local displacement. We use MC dropout with 200 stochastic passes to quantify uncertainty of predictions. To validate our approach, we define a baseline model without dropout and compare it to a stochastic model using 10-fold cross-validation. Given a starting planned trajectory, we predicted electrode bending using inferred local displacement at the tip via simulation. We found MC dropout performed better than a non-stochastic baseline model and provided confidence intervals along the predicted trajectory of electrodes. We believe this approach facilitates better decision making for electrode bending prediction in surgical planning.

KW - epilepsy

KW - neural network

KW - stereotactic neurosurgery

KW - trajectory prediction

KW - uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85085860533&partnerID=8YFLogxK

U2 - 10.1109/ISBI45749.2020.9098730

DO - 10.1109/ISBI45749.2020.9098730

M3 - Conference paper

AN - SCOPUS:85085860533

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 674

EP - 677

BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020

Y2 - 3 April 2020 through 7 April 2020

ER -