Identifcation of anatomical vessel branches is a prerequisite task for diagnosis, treatment and inter-subject comparison. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our method frst extracts bifurcations of interest from the centerlines of vessels, where a set of geometric features are also calculated from. Then the probability distribution of every bifurcation is learned using a XGBoost classifer. Finally a Hidden Markov Model with a restricted ...
Identifcation of anatomical vessel branches is a prerequisite task for diagnosis, treatment and inter-subject comparison. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our method frst extracts bifurcations of interest from the centerlines of vessels, where a set of geometric features are also calculated from. Then the probability distribution of every bifurcation is learned using a XGBoost classifer. Finally a Hidden Markov Model with a restricted transition strategy is constructed in order to nd the most likely labeling confguration of the whole structure, while also enforcing topological consistency. In this paper, the proposed approach has been evaluated through leave-one-out cross validation on 50 subjects of centerlines obtained from MRA images of healthy volunteers' Circle of Willis. Results demonstrate that our method can achieve higher accuracy and specifcity, while obtaining similar precision and recall, when comparing to the best performing state-of-the-art methods. Our algorithm can handle diferent topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment. Source code and data are available under.
+
