Open AccessAutomatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography
Author(s) -
DuckJong Han,
Hackjoon Shim,
Bo-Young Jeon,
Yeonggul Jang,
Yong Woo Hong,
SungHee Jung,
Seongmin Ha,
Hyuk Jae Chang
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0156837
Subject(s) - coronary arteries , segmentation , voxel , artificial intelligence , computer vision , computer science , boundary (topology) , artery , computed tomographic angiography , angiography , lumen (anatomy) , right coronary artery , coronary vessel , medicine , radiology , mathematics , coronary angiography , cardiology , myocardial infarction , mathematical analysis
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.