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.

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