International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2014: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 pp 651-658 | Cite as

SEEG Trajectory Planning: Combining Stability, Structure and Scale in Vessel Extraction

  • Maria A. Zuluaga
  • Roman Rodionov
  • Mark Nowell
  • Sufyan Achhala
  • Gergely Zombori
  • Manual Jorge Cardoso
  • Anna Miserocchi
  • Andrew W. McEvoy
  • John S. Duncan
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity [2]. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88±0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC=0.78±0.02).

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References

  1. 1.
    David, O., Blauwblomme, T., Job, A.S., Chabardès, S., Hoffmann, D., Minotti, L., Kahane, P.: Imaging the seizure onset zone with stereo-electroencephalography. Brain 134, 2898–2911 (2011)CrossRefGoogle Scholar
  2. 2.
    Olivier, A., Boling, W.W., Tanriverdi, T.: Techniques in epilepsy surgery: the MNI approach. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  3. 3.
    Bériault, S., Al Subaie, F., Collins, D.L., Sadikot, A.F., Pike, G.B.: A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int. J. CARS 7, 687–704 (2012)CrossRefGoogle Scholar
  4. 4.
    Essert, C., Haegelen, C., Lalys, F., Abadie, A., Jannin, P.: Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach. Int. J. Comput. Assist. Radiol. Surg. 7(4), 517–532 (2012)CrossRefGoogle Scholar
  5. 5.
    Shamir, R.R., Joskowicz, L., Tamir, I., Dabool, E., Pertman, L., Ben-Ami, A., Shoshan, Y.: Reduced risk trajectory planning in image guided keyhole neurosurgery. Med. Phys. 39, 2885–2895 (2012)CrossRefGoogle Scholar
  6. 6.
    Du, X., Ding, H., Zhou, W., Zhang, G., Wang, G.: Cerebrovascular segmentation and planning of depth electrode insertion for epilepsy surgery. Int. J. CARS 8, 905–916 (2013)CrossRefGoogle Scholar
  7. 7.
    Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: A Review of 3D Vessel Lumen Segmentation Techniques: Models, Features and Extraction Schemes. Med. Image Anal. 13, 819–845 (2009)CrossRefGoogle Scholar
  8. 8.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale Vessel Enhancement Filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Manniesing, R., Viergever, M., Niessen, W.: Vessel enhancing diffusion: A scale space representation of vessel structures. Med. Image Anal. 10, 815–825 (2006)CrossRefGoogle Scholar
  10. 10.
    Passat, N., Ronse, C., Baruthio, J., Armspach, J.-P., Foucher, J.: Watershed and multimodal data for brain vessel segmentation: Application to the superior sagittal sinus. Image Vision Comput. 25, 512–521 (2007)CrossRefMATHGoogle Scholar
  11. 11.
    Hu, Z., Niemeijer, M., Abràmoff, M.D., Garvin, M.K.: Multimodal Retinal Vessel Segmentation From Spectral-Domain Optical Coherence Tomography and Fundus Photography. IEEE Trans. Med. Imag. 31, 1900–1911 (2012)CrossRefGoogle Scholar
  12. 12.
    Zombori, G., et al.: A computer assisted planning system for the placement of sEEG electrodes in the treatment of epilepsy. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds.) IPCAI 2014. LNCS, vol. 8498, pp. 118–127. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  13. 13.
    Medioni, G., Lee, M.-S., Tang, C.-K.: A Computational Framework for Segmentation and Grouping. Elsevier Science (2000)Google Scholar
  14. 14.
    Moreno, R., Garcia, M.A., Puig, D., Pizarro, L., Burgeth, B., Weickert, J.: On improving the efficiency of tensor voting. IEEE Trans. Pattern Anal. Machine Intell. 33, 2215–2228 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria A. Zuluaga
    • 1
  • Roman Rodionov
    • 2
    • 3
  • Mark Nowell
    • 2
    • 3
  • Sufyan Achhala
    • 2
  • Gergely Zombori
    • 1
  • Manual Jorge Cardoso
    • 1
  • Anna Miserocchi
    • 2
    • 3
  • Andrew W. McEvoy
    • 1
  • John S. Duncan
    • 2
    • 3
  • Sébastien Ourselin
    • 1
  1. 1.Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.Dept. of Clinical and Experimental EpilepsyUCL IoNLondonUK
  3. 3.National Hospital for Neurology and Neurosurgery (NHNN)LondonUK

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