Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis.

The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

Collision detection and untangling for surgical robotic manipulators.

BACKGROUND: Robotic-assisted minimally invasive surgery provides several advantages over traditional surgery; however, it also has several drawbacks, such as possible collisions between the robotic arms and a limited field of view. METHODS: A generic method for tracking the configuration of a surgical manipulator in real time is presented. It is coupled with a collision detection and dynamic simulation algorithm, allowing the operator to detect collisions between robotic arms before they happen and presenting the best possible untangling direction to get out of collisions. RESULTS: Our algorithm successfully tracks the configuration of the Zeus surgical system and accurately detects possible collisions in real time. A pilot study on our system proved its efficiency in reducing the duration and severity of collisions, at the price of longer completion times. CONCLUSION: Our system helps alleviate the collision problem by reducing the time in collision.