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Workshop on Clinical Image-Based Procedures

International Workshop on Multimodal Learning for Clinical Decision Support

CLIP 2020, ML-CDS 2020: Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures pp 34–43Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((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.

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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.

Author information

Authors and Affiliations

  1. Czech Technical University in Prague, 160 00, Prague, Czech Republic

    Igor Varga, Eduard Bakstein & Daniel Novak

  2. Western University, Ontario, ON, N6A 3K7, Canada

    Greydon Gilmore

  3. National Institute of Mental Health, 50 67, Klecany, Czech Republic

    Eduard Bakstein

Authors
  1. Igor Varga
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  2. Eduard Bakstein
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  3. Greydon Gilmore
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  4. Daniel Novak
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Corresponding authors

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

Editor information

Editors and Affiliations

  1. IBM Almaden Research Center, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  2. Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  3. Tel Aviv University, Ramat Aviv, Israel

    Hayit Greenspan

  4. Case Western Reserve University, Cleveland, OH, USA

    Anant Madabhushi

  5. IBM Almaden Research Center, San Jose, CA, USA

    Alexandros Karargyris

  6. Children’s National Hospital, Washington, DC, USA

    Marius George Linguraru

  7. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Cristina Oyarzun Laura

  8. Children’s National Hospital, Washington, DC, USA

    Raj Shekhar

  9. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Stefan Wesarg

  10. Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

  11. Fraunhofer Singapore, Singapore, Singapore

    Marius Erdt

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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: Syeda-Mahmood, T., 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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60945-0

  • Online ISBN: 978-3-030-60946-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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