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Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12445)

Abstract

Identification of subcortical structures is an essential step in surgical planning for interventions such as the deep brain stimulation (DBS), in which permanent electrode is implanted in a precisely defined location. For refinement of the target localisation and compensation of brain shift occurring during the surgery, intra-operative electrophysiological recording using microelectrodes is usually undertaken.

In this paper, we present a multimodal method that consists of a) subthalamic nucleus (STN) segmentation from magnetic resonance T2 images using 3D active contour fitting and b) a subsequent brain shift compensation step, increasing the accuracy of microelectrode placement localisation by the probabilistic electrophysiology-based fitting. The method is evaluated on a data set of 39 multi-electrode trajectories from 20 patients undergoing DBS surgery for Parkinson’s disease in a leave-one-subject-out scenario. The performance comparison shows increased sensitivity and slightly decreased specificity of STN identification using the individually-segmented 3D contours, compared to electrophysiology-based refinement of a standard 3D atlas.

To achieve accurate segmentation from the low-resolution clinical T2 images, a more sophisticated approach, including shape priors and intensity model, needs to be implemented. However, the presented approach is a step towards automatic identification of microelectrode recording sites and possibly also an assistive system for the DBS surgery.

Keywords

  • Active contours
  • Deep brain stimulation
  • Surface fitting
  • Subthalamic nucleus

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Acknowledgments

The study was supported by the Research Centre for Informatics, grant number CZ.02.1.01/0.0/16~019/0000765 and by the grant Biomedical data acquisition, processing and visualisation, number SGS19/171/OHK3/3T/13. The work of EB has been supported by the Ministry of Health of the Czech Republic under the grant NV19-04-00233.

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Correspondence to Igor Varga , Eduard Bakstein , Greydon Gilmore or Daniel Novak .

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Varga, I., Bakstein, E., Gilmore, G., Novak, D. (2020). Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement. In: , et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-60946-7_4

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