title: Computerized analysis of coronary artery disease: performance evaluation of segmentation and tracking of coronary arteries in CT angiograms.
abstract: OBJECTIVE
  The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors' coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques.
  
  
  METHODS
  The heart region in cCTA is segmented and the vascular structures are enhanced using the authors' multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors' patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments.
  
  
  RESULTS
  The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases.
  
  
  CONCLUSIONS
  The authors' MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Aamer
authors.initials: A
authors.lastname: Chughtai
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Jean
authors.initials: J
authors.lastname: Kuriakose
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Prachi
authors.initials: P
authors.lastname: Agarwal
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Ella A
authors.initials: EA
authors.lastname: Kazerooni
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Smita
authors.initials: S
authors.lastname: Patel
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
mesh_terms: D016477:Artifacts; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods
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