Advertisement

Validation of Catheter Segmentation for MR-Guided Gynecologic Cancer Brachytherapy

  • Guillaume Pernelle
  • Alireza Mehrtash
  • Lauren Barber
  • Antonio Damato
  • Wei Wang
  • Ravi Teja Seethamraju
  • Ehud Schmidt
  • Robert A. Cormack
  • Williams Wells
  • Akila Viswanathan
  • Tina Kapur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

Abstract

Segmentation of interstitial catheters from MRI needs to be addressed in order for MRI-based brachytherapy treatment planning to become part of the clinical practice of gynecologic cancer radiotherapy. This paper presents a validation study of a novel image-processing method for catheter segmentation. The method extends the distal catheter tip, interactively provided by the physician, to its proximal end, using knowledge of catheter geometry and appearance in MRI sequences. The validation study consisted of comparison of the algorithm results to expert manual segmentations, first on images of a phantom, and then on patient MRI images obtained during MRI-guided insertion of brachytherapy catheters for the treatment of gynecologic cancer. In the phantom experiment, the maximum disagreement between automatic and manual segmentation of the same MRI image, as computed using the Hausdorf distance, was 1.5 mm, which is of the same order as the MR image spatial resolution, while the disagreement between automatic segmentation of MR images and “ground truth”, manual segmentation of CT images, was 3.5mm. The segmentation method was applied to an IRB-approved retrospective database of 10 interstitial brachytherapy patients which included a total of 101 catheters. Compared with manual expert segmentations, the automatic method correctly segmented 93 out of 101 catheters, at an average rate of 0.3 seconds per catheter using a 3GHz Intel Core i7 computer with 16 GB RAM and running Mac OS X 10.7. These results suggest that the proposed catheter segmentation is both technically and clinically feasible.

Keywords

validation segmentation catheter MRI 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Viswanathan, A.N., Szymonifka, J., Tempany-Afdhal, C., Cormack, R.A.: A prospective trial of real-time magnetic resonance-guided catheter placement in interstitial gynecologic brachytherapy. Brachytherapy (Epub. 2013)Google Scholar
  3. 3.
    Poetter, R., Haie-Meder, C., Van Limbergen, E., et al.: Recommendations from gynaecological (GYN) GEC ESTRO working group. Radiother Oncol. 78(1), 67–77 (2006); Epub 2006 January 5. PubMed PMID: 16403584Google Scholar
  4. 4.
    Cleary, K., Peters, T.M.: Image-guided interventions: technology review and clinical applications. Annu. Rev. Biomed. Eng. 12, 119–142 (2010)CrossRefGoogle Scholar
  5. 5.
    Lewin, J.S.: Interventional MR imaging: concepts, systems, and applications in neuroradiology. AJNR Am. J. Neuroradiol. 20(5), 735–748 (1999); Review. PubMed PMID: 10369339Google Scholar
  6. 6.
    Song, S.E., Cho, N.B., Iordachita, I.I., Guion, P., Fichtinger, G., Kaushal, A., Camphausen, K., Whitcomb, L.L.: Biopsy needle artifact localization in MRI-guided robotic transrectal prostate intervention. IEEE Trans. Biomed. Eng. 59(7), 1902–1911 (2012); PubMed PMID: 22481805Google Scholar
  7. 7.
    DiMaio, S.P., Kacher, D.F., Ellis, R.E., Fichtinger, G., Hata, N., Zientara, G.P., Panych, L.P., Kikinis, R., Jolesz, F.A.: Catheter Artifact Localization in 3T MR Images. Stud. Health Technol. Inform. 119, 120–125 (2006); PMID: 16404029Google 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.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Kikinis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2(2), 143–168 (1998)CrossRefGoogle Scholar
  10. 10.
    Okazawa, S.H., Ebrahimi, R., Chuang, J., Rohling, R.N., Salcudean, S.E.: Methods for segmenting curved catheters in ultrasound images. Med. Image Anal. 10(3), 330-342 (2006); Epub 2006 March 7. PubMed PMID: 16520082Google Scholar
  11. 11.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993), doi:10.1109/34.232073CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guillaume Pernelle
    • 1
    • 2
  • Alireza Mehrtash
    • 2
  • Lauren Barber
    • 2
  • Antonio Damato
    • 3
  • Wei Wang
    • 3
  • Ravi Teja Seethamraju
    • 3
  • Ehud Schmidt
    • 2
  • Robert A. Cormack
    • 2
  • Williams Wells
    • 2
  • Akila Viswanathan
    • 2
  • Tina Kapur
    • 2
  1. 1.Technische Universität MünchenGermany
  2. 2.Brigham & Women’s Hospital and Harvard Medical SchoolUSA
  3. 3.Siemens HealthcareUSA

Personalised recommendations