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Model-based Catheter Segmentation in MRI-images

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.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2015
Publication Date:
Int Conf Med Image Comput Comput Assist Interv. 2015 Oct;18(WS). Workshop on Interactive Medical Image Computing (IMIC).
segmentation, identification, catheter, MRI, validation
Appears in Collections:
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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