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Automatic Labeling of Vascular Structures with Topological Constraints via HMM

  • Xingce Wang
  • Yue Liu
  • Zhongke WuEmail author
  • Xiao Mou
  • Mingquan Zhou
  • Miguel A. González Ballester
  • Chong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

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.

Notes

Acknowledgments

This research was partially supported by the Chinese High-Technical Research Development Foundation (863) Program (No. 2015AA020506), Beijing Natural Science Foundation of China (No. 4172033), the Spanish Ministry of Economy and Competitiveness, through the Maria de Maeztu Programme for Centres/Units of Excellence in R&D (MDM-2015-0502), and the Spanish Ministry of Economy and Competitiveness (DEFENSE project, TIN2013-47913-C3-1-R). We thank the authors of [2] for sharing their centerline delineations.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xingce Wang
    • 1
  • Yue Liu
    • 1
  • Zhongke Wu
    • 1
    Email author
  • Xiao Mou
    • 1
  • Mingquan Zhou
    • 1
  • Miguel A. González Ballester
    • 2
    • 3
  • Chong Zhang
    • 2
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.SimBioSys, DTICUniversitat Pompeu FabraBarcelonaSpain
  3. 3.ICREABarcelonaSpain

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