Skip to main content

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)


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-60946-7_4
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-60946-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   74.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.


  1. Bakštein, E., Sieger, T., Novák, D., Růžička, F., Jech, R.: Automated atlas fitting for deep brain stimulation surgery based on microelectrode neuronal recordings. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds.) World Congress on Medical Physics and Biomedical Engineering 2018. IP, vol. 68/3, pp. 105–111. Springer, Singapore (2019).

    CrossRef  Google Scholar 

  2. Bakštein, E., Sieger, T., Růžička, F., Novák, D., Jech, R.: Fusion of microelectrode neuronal recordings and MRI landmarks for automatic atlas fitting in deep brain stimulation surgery. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 175–183. Springer, Cham (2018).

    CrossRef  Google Scholar 

  3. Bakštein, E., et al.: Methods for automatic detection of artifacts in microelectrode recordings. J. Neurosci. Meth. 290, 39–51 (2017)

    CrossRef  Google Scholar 

  4. Bjerknes, S., et al.: Multiple microelectrode recordings in STN-DBS surgery for Parkinson’s disease: a randomized study. Mov. Disord. Clin. Pract. 5(3), 296–305 (2018)

    Google Scholar 

  5. Chan, T., Vese, L.: An active contour model without edges. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, pp. 141–151. Springer, Heidelberg (1999).

    CrossRef  Google Scholar 

  6. Chen, X., Williams, B.M., Vallabhaneni, S.R., Czanner, G., Williams, R., Zheng, Y.: Learning active contour models for medical image segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11624–11632. IEEE, Long Beach, CA, USA, June 2019

    Google Scholar 

  7. Coenen, V.A., Prescher, A., Schmidt, T., Picozzi, P., Gielen, F.L.H.: What is dorso-lateral in the subthalamic Nucleus (STN)?–a topographic and anatomical consideration on the ambiguous description of today’s primary target for deep brain stimulation (DBS) surgery. Acta Neurochir. (Wien) 150(11), 1163–1165 (2008)

    CrossRef  Google Scholar 

  8. Groiss, S., Wojtecki, L., Südmeyer, M., Schnitzler, A.: Review: deep brain stimulation in Parkinson’s disease. Ther. Adv. Neurol. Disord. 2(6), 379–391 (2009)

    CrossRef  Google Scholar 

  9. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)

    CrossRef  Google Scholar 

  10. Marquez-Neila, P., Baumela, L., Alvarez, L.: A morphological approach to curvature-based evolution of curves and surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 2–17 (2014)

    CrossRef  Google Scholar 

  11. Moran, A., Bar-Gad, I., Bergman, H., Israel, Z.: Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure. Mov. Disord. 21(9), 1425–1431 (2006)

    CrossRef  Google Scholar 

  12. Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56(3), 907–922 (2011)

    CrossRef  Google Scholar 

  13. Reinhold, J.C., Dewey, B.E., Carass, A., Prince, J.L.: Evaluating the impact of intensity normalization on MR image synthesis. arXiv:1812.04652 [cs], December 2018. arXiv: 1812.04652

  14. Sieger, T., et al.: Distinct populations of neurons respond to emotional valence and arousal in the human subthalamic nucleus. Proc. Natl. Acad. Sci. 112(10), 3116–3121 (2015)

    CrossRef  Google Scholar 

  15. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)

    CrossRef  Google Scholar 

  16. Visser, E., Keuken, M.C., Forstmann, B.U., Jenkinson, M.: Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7 T data at young and old age. Neuroimage 139, 324–336 (2016)

    CrossRef  Google Scholar 

  17. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)

    CrossRef  Google Scholar 

  18. Zwirner, J., et al.: Subthalamic nucleus volumes are highly consistent but decrease age-dependently-a combined magnetic resonance imaging and stereology approach in humans. Hum. Brain Mapp. 38(2), 909–922 (2017)

    CrossRef  Google Scholar 

Download references


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


Corresponding authors

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

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • 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)