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Validation of Catheter Segmentation for MR-guided Gynecologic Cancer Brachytherapy

1Technische University of Munchen, Germany.
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
3Siemens Healthcare, Forchheim, Germany.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Volume Number:
Issue Number:
Pt 3
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):380-7.
PubMed ID:
Validation, segmentation, catheter, MRI
Appears in Collections:
R03 EB013792/EB/NIBIB NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Pernelle G., Mehrtash A., Barber L., Damato A., Wang W., Seethamraju R.T., Schmidt E.J., Cormack R., Wells III W.M., Viswanathan A.N., Kapur T. Validation of Catheter Segmentation for MR-guided Gynecologic Cancer Brachytherapy. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):380-7. PMID: 24505784. PMCID: PMC4005335.
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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.

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