Wang X, Liu Y, Wu Z, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. MICCAI2017
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository as well as at the UPF e-repository
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.
Additional material: