Wang X, Liu Y, Wu Z, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. MICCAI2017
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Wang X, Liu Y, Wu Z, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. MICCAI2017
Wang X, Liu Y, Wu Z, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. MICCAI2017 (accepted)
Identification of anatomical branches of vascular structures 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 first extracts bifurcations of interest from the centerlines of vessel tree structures, where a set of geometric features are also calculated. Then the probability distribution of these bifurcations is learned using a XGBoost classifier. Finally a Hidden Markov Model with a restricted transition strategy is constructed in order to find the most likely labeling configuration of the whole structure, while also enforcing topological consistency. In this paper, the proposed algorithm has been evaluated through leave-one-out cross validation on 50 subjects of centerline models obtained from MRA images of healthy volunteers’ Circle of Willis. Results demonstrate that our method can achieve higher accuracy and specificity than the best performing stateof-the-art methods, while obtaining similar precision and recall. It is also worth noting that our algorithm can handle different topologies, like circle, chain and tree. By using scale and coordinate independent geometrical features, our method does not require global alignment as a preprocessing step.
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