Automatic Labeling of Vascular Structures with Topological Constraints via HMM

  • Authors
  • Wang, Xingce; Liu, Yue; Wu, Zhongke; Mou, Xiao; Zhou, Mingquan; González Ballester, Miguel Ángel; Zhang, Chong
  • UPF authors
  • GONZALEZ BALLESTER, MIGUEL ANGEL; ZHANG ., CHONG;
  • Type
  • Scholarly articles
  • Journal títle
  • Lecture Notes in Computer Science / Artificial Intelligence
  • Publication year
  • 2017
  • Volume
  • 10434
  • Pages
  • 208-215
  • ISSN
  • 0302-9743
  • Publication State
  • Published
  • Abstract
  • Identification 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 first 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 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 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 specificity, while obtaining similar precision and recall, when comparing to the best performing state-of-the-art methods. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment.
  • Complete citation
  • Wang, Xingce; Liu, Yue; Wu, Zhongke; Mou, Xiao; Zhou, Mingquan; González Ballester, Miguel Ángel; Zhang, Chong. Automatic Labeling of Vascular Structures with Topological Constraints via HMM. Lecture Notes in Computer Science / Artificial Intelligence 2017; 10434( ): 208-215.
Bibliometric indicators
  • 2 times cited Scopus
  • Índex Scimago de 0.295(2017)