Title:
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Automatic labeling of vascular structures with topological constraints via HMM
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Author:
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Wang, Xingce; Liu, Yue; Wu, Zhongke; Mou, Xiao; Zhou, Mingquan; González Ballester, Miguel Ángel, 1973-; Zhang, Chong
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Abstract:
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Comunicació presentada a: the 20th International Conference on Medical Image Computing and Computer Assisted Intervention 2017 (MICCAI 2017), celebrada del 10 al 14 de setembre de 2017 a Quebec, Canadà. |
Abstract:
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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
http://doi.org/10.5281/zenodo.809931. |
Abstract:
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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). |
Subject(s):
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-Vessel labeling -Topological constraints -Hidden Markov Model -XGBoost -Circle of Willis |
Rights:
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© Springer The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-66185-8_24 |
Document type:
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Conference Object Article - Accepted version |
Published by:
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Springer
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