TY - GEN
T1 - Post-operative electrode placement prediction in deep brain stimulation using support vector regression
AU - Singer, Alexa
AU - Zhang, Chencheng
AU - Wang, Tao
AU - Qiu, Suhao
AU - Li, Dianyou
AU - Du, Yiping
AU - Liang, Zhi Pei
AU - Herman, Pawel
AU - Sun, Bomin
AU - Feng, Yuan
N1 - Funding Information:
5. ACKNOWELEGDEMENTS Grant 3187094 from NSFC and grant 1944190700 from Shanghai Science and Technology Committee (STCSM) are acknowledged.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/24
Y1 - 2019/8/24
N2 - Deep brain stimulation (DBS) is a neurosurgical procedure for treating neurodegenerative diseases and neurological disorders such as Parkinson’s disease (PD) and epilepsy. Image guidance is crucial for the accurate placement of DBS electrodes. However, current surgical planning systems based on preoperative MR and CT images of the brain cannot take into account the intra-operative brain-shift, resulting in suboptimal electrode placement undermining clinical outcomes. In this study, a support vector regression (SVR) model was constructed based on 114 patient-specific data of PD patients. Two target nuclei were manually delineated based on pre-operative MR and CT images. Spatial coordinates of the two nuclei were collected and compared to the post-surgical electrode position from CT images. Analysis of a total of 45 features showed that the pre-operative target coordinates are the parameters mainly influencing the model prediction for both nuclei. The mean absolute error (MAE) of the prediction of the electrodes on unseen patients was 0.76mm. This study demonstrates the potential of using SVR modelling to improve current DBS surgical planning procedure and preoperative risk-assessment.
AB - Deep brain stimulation (DBS) is a neurosurgical procedure for treating neurodegenerative diseases and neurological disorders such as Parkinson’s disease (PD) and epilepsy. Image guidance is crucial for the accurate placement of DBS electrodes. However, current surgical planning systems based on preoperative MR and CT images of the brain cannot take into account the intra-operative brain-shift, resulting in suboptimal electrode placement undermining clinical outcomes. In this study, a support vector regression (SVR) model was constructed based on 114 patient-specific data of PD patients. Two target nuclei were manually delineated based on pre-operative MR and CT images. Spatial coordinates of the two nuclei were collected and compared to the post-surgical electrode position from CT images. Analysis of a total of 45 features showed that the pre-operative target coordinates are the parameters mainly influencing the model prediction for both nuclei. The mean absolute error (MAE) of the prediction of the electrodes on unseen patients was 0.76mm. This study demonstrates the potential of using SVR modelling to improve current DBS surgical planning procedure and preoperative risk-assessment.
KW - Brain-shift
KW - Deep brain stimulation
KW - Electrode misplacement
KW - Support vector regression
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U2 - 10.1145/3364836.3364876
DO - 10.1145/3364836.3364876
M3 - Conference contribution
AN - SCOPUS:85077521492
T3 - ACM International Conference Proceeding Series
SP - 202
EP - 207
BT - ISICDM 2019 - Conference Proceedings
PB - Association for Computing Machinery
T2 - 3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019
Y2 - 24 August 2019 through 26 August 2019
ER -