Skip to main content

Advertisement

Log in

Cerebrovascular segmentation and planning of depth electrode insertion for epilepsy surgery

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Depth electrodes are inserted in the brain to locate the epileptogenic zone without craniotomy, but there is risk of surgical hemorrhage. Preoperative planning is required to mitigate this risk. A preoperative imaging, segmentation and three dimensional (3D) visualization procedure was developed to provide neurosurgeons with cortical and vascular anatomy information for surgical planning and neuronavigation.

Methods

Cerebral vascular imaging was performed with phase-contrast magnetic resonance angiography (PC-MRA). Fuzzy c-means was performed to extract brain parenchyma from the PC-MRA images. A multi-scale vessel enhancement filter and thresholding process were combined to segment the vasculature and suppress background noise in the PC-MRA images. Finally, 3D visualization of the vasculature and cortical structures was implemented using volume rendering.

Results

Quantitative and qualitative validation of the vascular segmentation method were done. Using manual vascular segmentation as the gold standard, our method produced a satisfactory result: sensitivity was as high as 90 % at a specificity level of 95 %. Moreover, comparing the 3D visualizations of the vasculature and cortical structure for 4 patients with their respective intraoperative craniotomy photographs showed high levels of similarity.

Conclusion

A new automated segmentation and visualization procedure provides sufficient and accurate cortical and vascular anatomy information compared to intraoperative photographs. This method has potential to assist neurosurgeons with planning and neuronavigation for depth electrode insertion with avoidance of cerebral hemorrhage.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sirven JI, Pedley TA, Wilterdink JL (2011) Evaluation and management of drug-resistant epilepsy. Available at: http://www.uptodate.com/contents/evaluation-and-management-of-drug-resistant-epilepsy. Accessed Apr 15 2011

  2. Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Sign Process Mag 18(6):14–30

    Article  Google Scholar 

  3. Rosenfeld JV (1996) Minimally invasive neurosurgery. Aust N Z J Surg 66(8):553–559

    Article  PubMed  CAS  Google Scholar 

  4. Olivier A, Boling WW, Tanriverdi T (2012) Techniques in epilepsy surgery: the MNI approach. Cambridge University Press, Cambridge

    Book  Google Scholar 

  5. Brontë-Stewart H, Louie S, Batya S, Henderson JM (2010) Clinical motor outcome of bilateral subthalamic nucleus deep-brain stimulation for Parkinson’s disease using image-guided frameless stereotaxy. Neurosurgery 67(4):1088

    Article  PubMed  Google Scholar 

  6. Chakravarty MM, Sadikot AF, Mongia S, Bertrand G, Collins DL (2006) Towards a multi-modal atlas for neurosurgical planning. In: Medical image computing and computer-assisted intervention-MICCAI 2006. Springer, pp 389–396

  7. Holloway KL, Gaede SE, Starr PA, Rosenow JM, Ramakrishnan V, Henderson JM (2005) Frameless stereotaxy using bone fiducial markers for deep brain stimulation. J Neurosurg 103(3):404–413

    Article  PubMed  Google Scholar 

  8. Machado A, Rezai AR, Kopell BH, Gross RE, Sharan AD, Benabid AL (2006) Deep brain stimulation for Parkinson’s disease: surgical technique and perioperative management. Mov Disord 21(S14):S247–S258

    Article  PubMed  Google Scholar 

  9. Liu Y, Dawant BM, Pallavaram S, Neimat JS, Konrad PE, D’Haese P-F, Datteri RD, Landman BA, Noble JH A surgeon specific automatic path planning algorithm for deep brain stimulation. In: SPIE medical imaging, 2012. international society for optics and photonics, pp 83161D–83161D

  10. Brunenberg EJ, Vilanova A, Visser-Vandewalle V, Temel Y, Ackermans L, Platel B, ter Haar Romeny BM (2007) Automatic trajectory planning for deep brain stimulation: a feasibility study. In: Medical image computing and computer-assisted intervention-MICCAI 2007. Springer, pp 584–592

  11. Essert C, Haegelen C, Lalys F, Abadie A, Jannin P (2012) 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

    Article  PubMed  Google Scholar 

  12. Shamir RR, Joskowicz L, Tamir I, Dabool E, Pertman L, Ben-Ami A, Shoshan Y (2012) Reduced risk trajectory planning in image-guided keyhole neurosurgery. Med Phys 39:2885

    Article  PubMed  Google Scholar 

  13. Bériault S, Al Subaie F (2012) A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7(5):687–704

    Article  PubMed  Google Scholar 

  14. Smith ML, Elliott IM, Lach L (2002) Cognitive skills in children with intractable epilepsy: comparison of surgical and nonsurgical candidates. Epilepsia 43(6):631–637

    Article  PubMed  Google Scholar 

  15. Campeau NG, Huston J III (2001) Magnetic resonance angiography at 3.0 Tesla: initial clinical experience. Top Magn Reson Imag 12(3):183–204

    Article  CAS  Google Scholar 

  16. Essig M, Giesel FL (2008) Head and neck MRA. In: Clinical blood pool MR imaging. Springer, pp 59–68

  17. Maki JH, Chenevert TL, Prince MR (1996) Three-dimensional contrast-enhanced MR angiography. Top Magn Reson Imag 8(6):322–344

    Article  CAS  Google Scholar 

  18. Thomsen HS, Marckmann P, Logager VB (2007) Nephrogenic systemic fibrosis (NSF): a late adverse reaction to some of the gadolinium based contrast agents. Cancer Imag 7(1):130

    Article  Google Scholar 

  19. Vu D, González RG, Schaefer PW (2006) Conventional MRI and MR angiography of stroke. In: Acute ischemic stroke. Springer, pp 115–137

  20. Nishimura DG (2005) Time-of-flight MR angiography. Magn Reson Med 14(2):194–201

    Article  Google Scholar 

  21. Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM (1997) Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 204(1):272–277

    PubMed  CAS  Google Scholar 

  22. Yucel EK (1995) Magnetic resonance angiography: a practical approach. McGraw-Hill, Health Professions Division, New York

    Google Scholar 

  23. Ng A, Johnston L, Chen Z, Cho ZH, Zhang J, Egan G (2011) Spatially dependent filtering for removing phase distortions at the cortical surface. Magn Reson Med 66(3):784–793

    Article  PubMed  Google Scholar 

  24. Dumoulin C (1995) Phase contrast MR angiography techniques. Magn Reson Imag Clin N Am 3(3):399

    CAS  Google Scholar 

  25. Pernicone J, Siebert JE, Potchen E, Pera A, Dumoulin C, Souza S (1990) Three-dimensional phase-contrast MR angiography in the head and neck: preliminary report. Am J Roentgenol 155(1): 167–176

    Google Scholar 

  26. Ross DA, Brunberg JA, Drury I, Henry TR (1996) Intracerebral depth electrode monitoring in partial epilepsy: the morbidity and efficacy of placement using magnetic resonance image-guided stereotactic surgery. Neurosurgery 39(2):327–334

    Google Scholar 

  27. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203

    Google Scholar 

  28. Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3(5):672–682

    Article  PubMed  CAS  Google Scholar 

  29. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Medical image computing and computer-assisted interventation-MICCAI’98. Springer, pp 130–137

  30. Enquobahrie A, Ibanez L, Bullitt E, Aylward S (2007) Vessel enhancing diffusion filter. Insight J. http://www.insight-journal.com/download/pdf/6321/VEDArticle2.pdf

  31. Levoy M (1990) Efficient ray tracing of volume data. ACM Trans Graph (TOG) 9(3):245–261

    Article  Google Scholar 

  32. Marks MP, Pelc NJ, Ross MR, Enzmann DR (1992) Determination of cerebral blood flow with a phase-contrast cine MR imaging technique: evaluation of normal subjects and patients with arteriovenous malformations. Radiology 182(2):467–476

    Google Scholar 

  33. Liu W, Guo H, Du X, Zhou W, Zhang G, Ding H, Wang G (2012) Cortical vessel imaging and visualization for image guided depth electrode insertion. Comput Med Imag Graph. doi:10.1016/j.compmedimag.2012.05.004

  34. Axel L (1984) Blood flow effects in magnetic resonance imaging. Am J Roentgenol 143(6):1157–1166

    Article  CAS  Google Scholar 

  35. Xiao C, Staring M, Shamonin D, Reiber JHC, Stolk J, Stoel BC (2011) A strain energy filter for 3D vessel enhancement with application to pulmonary CT images. Med Image anal 15(1):112–124

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported in part by grants from National Basic Research and key Technologies R&D Program of China (2011CB707701, 2012BAI16B03), National Natural Science Foundation of China (81127003) and Tsinghua University Initiative Scientific Research Program.

Conflict of Interest

None.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guangzhi Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Du, X., Ding, H., Zhou, W. et al. Cerebrovascular segmentation and planning of depth electrode insertion for epilepsy surgery. Int J CARS 8, 905–916 (2013). https://doi.org/10.1007/s11548-013-0843-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-013-0843-5

Keywords

Navigation