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Towards Uncertainty Quantification for Electrode Bending Prediction in Stereotactic Neurosurgery

Research output: Chapter in Book/Report/Conference proceedingConference paper

Alejandro Granados, Oeslle Lucena, Vejay Vakharia, Anna Miserocchi, Andrew W. McEvoy, Sjoerd B. Vos, Roman Rodionov, John S. Duncan, Rachel Sparks, Sebastien Ourselin

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages674-677
Number of pages4
ISBN (Electronic)9781538693308
DOIs
Publication statusPublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period3/04/20207/04/2020

King's Authors

Abstract

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.

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