  Accurate and reliable segmentation of catheters in MR-guided interventions
remains a challenge, and a step of critical importance in clinical workflows.
In this work, under reasonable assumptions, mechanical 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 feature based candidate points. The final catheter
trajectories are hybrid sequences of individual points, each derived from model
and image features. 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 deviating 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.

Computer Vision and Pattern Recognition