TY - JOUR
T1 - Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning
AU - Granados, Alejandro
AU - Han, Yuxuan
AU - Lucena, Oeslle
AU - Vakharia, Vejay
AU - Rodionov, Roman
AU - Vos, Sjoerd B.
AU - Miserocchi, Anna
AU - McEvoy, Andrew W.
AU - Duncan, John S.
AU - Sparks, Rachel
AU - Ourselin, Sébastien
N1 - Funding Information:
This work was supported by the Wellcome Innovator Award (218380/Z/19/Z); the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare; and the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. The research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility (CRF) at Guys and St Thomas. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. We are grateful to the Wolfson Foundation and the Epilepsy Society for supporting the Epilepsy Society MRI scanner.
Funding Information:
This work was supported by the Wellcome Innovator Award (218380/Z/19/Z); the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare; and the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. The research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London and supported by the NIHR Clinical Research Facility (CRF) at Guys and St Thomas. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. We are grateful to the Wolfson Foundation and the Epilepsy Society for supporting the Epilepsy Society MRI scanner.
Publisher Copyright:
© 2021, The Author(s).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5
Y1 - 2021/5
N2 - 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 (lu) or electrode bending (eb^). 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 lu performed better than for eb^. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% (lu) and 39.9% (eb^), 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 lu. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE≤ 1 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.
AB - 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 (lu) or electrode bending (eb^). 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 lu performed better than for eb^. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% (lu) and 39.9% (eb^), 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 lu. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE≤ 1 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.
KW - Prediction of trajectory
KW - SEEG
KW - Surgical planning
UR - http://www.scopus.com/inward/record.url?scp=85103192335&partnerID=8YFLogxK
U2 - 10.1007/s11548-021-02347-8
DO - 10.1007/s11548-021-02347-8
M3 - Article
AN - SCOPUS:85103192335
SN - 1861-6410
VL - 16
SP - 789
EP - 798
JO - International Journal of Computer Assisted Radiology and Surgery
JF - International Journal of Computer Assisted Radiology and Surgery
IS - 5
ER -