Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography

We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with...

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Bibliographic Details
Published in:PloS one 2016-08, Vol.11 (8), p.e0156837-e0156837
Main Authors: Han, Dongjin, Shim, Hackjoon, Jeon, Byunghwan, Jang, Yeonggul, Hong, Youngtaek, Jung, Sunghee, Ha, Seongmin, Chang, Hyuk-Jae
Format: Article
Language:English
Subjects:
Algorithms
Angiography
Arteries
Automation
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Cardiovascular disease
Computed tomography
Computed Tomography Angiography - methods
Coronary Angiography - methods
Coronary arteries
Coronary artery
Coronary heart disease
Coronary vessels
Coronary Vessels - anatomy & histology
Coronary Vessels - diagnostic imaging
Diagnosis
Engineering and Technology
Filtration
Geometry
Humans
International conferences
Medical imaging
Medicine
Medicine and Health Sciences
Methods
Models, Theoretical
Neural networks
Physical Sciences
Public domain
Research and Analysis Methods
Segmentation
Segments
Stochastic Processes
Stochasticity
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Summary:We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0156837