Robust detection of bifurcations for vessel tree tracking

Xin Wang; Tobias Heimann; Hans-Peter Meinzer; Ingmar Wegner

doi:10.1117/12.878882

8 March 2011 Robust detection of bifurcations for vessel tree tracking

Xin Wang, Tobias Heimann, Hans-Peter Meinzer, Ingmar Wegner

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796327 (2011) https://doi.org/10.1117/12.878882
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Vessel tree tracking is an important and challenging task for many medical applications. This paper presents a novel bifurcation detection algorithm for Bayesian tracking of vessel trees. Based on a cylindrical model, we introduce a bifurcation metric that yields minimal values at potential branching points. This approach avoids searching for bifurcations in every iteration of the tracking process (as proposed by prior works) and is therefore computationally more efficient. We use the same geometric model for the bifurcation metric as for the tracking; no specific bifurcation model is needed. In a preliminary evaluation of our method on 8 CTA datasets of coronary arteries, all side branches and 95.8% of the main branches were detected correctly.

Citation Download Citation

Xin Wang, Tobias Heimann, Hans-Peter Meinzer, and Ingmar Wegner "Robust detection of bifurcations for vessel tree tracking", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796327 (8 March 2011); https://doi.org/10.1117/12.878882

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available