Validation of Catheter Segmentation for MR-guided Gynecologic Cancer Brachytherapy.

Citation:

Pernelle G, Mehrtash A, Barber L, Damato A, Wang W, Seethamraju RT, Schmidt E, Cormack RA, Wells III WM, Viswanathan A, et al. Validation of Catheter Segmentation for MR-guided Gynecologic Cancer Brachytherapy. Med Image Comput Comput Assist Interv. 2013;16 (Pt 3) :380-7. Copy at http://www.tinyurl.com/zmw8owd

Date Published:

2013

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 3.5 mm. 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 3 GHz 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.

Last updated on 10/07/2016