Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Model-based Catheter Segmentation in MRI-images

Institution:
1Inst. of Medical Informatics, University of Luebeck, Germany.
2Computational Neuroscience Lab, Imperial College, London, UK.
3Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2015
Publication Date:
Oct-2015
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2015 Oct;18(WS). Workshop on Interactive Medical Image Computing (IMIC).
Keywords:
segmentation, identification, catheter, MRI, validation
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Mastmeyer A., Pernelle G., Barber L., Pieper S., Fortmeier D., Wells III W.M., Handels H., Kapur T. Model-based Catheter Segmentation in MRI-images. Int Conf Med Image Comput Comput Assist Interv. 2015 Oct;18(WS). Workshop on Interactive Medical Image Computing (IMIC).
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Abstract. Accurate and reliable segmentation of catheters in MR-gui- ded interventions remains a challenge, and a step of critical importance in clinical workflows. In this work, under reasonable assumptions, me- chanical model based heuristics guide the segmentation process allows correct catheter identification rates greater than 98% (error 2.88 mm), and reduction in outliers to one-fourth compared to the state of the art. Given distal tips, searching towards the proximal ends of the catheters is guided by mechanical models that are estimated on a per-catheter basis. Their bending characteristics are used to constrain the image fea- ture based candidate points. The final catheter trajectories are hybrid sequences of individual points, each derived from model and image fea- tures. We evaluate the method on a database of 10 patient MRI scans including 101 manually segmented catheters. The mean errors were 1.40 mm and the median errors were 1.05 mm. The number of outliers devi- ating more than 2 mm from the gold standard is 7, and the number of outliers deviating more than 3 mm from the gold standard is just 2.

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