title: Semiautomatic segmentation of atherosclerotic carotid artery wall volume using 3D ultrasound imaging.
abstract: OBJECTIVE
  Rupture of atherosclerotic plaques in the carotid artery has been implicated in 20% of strokes. 3D ultrasound (US) imaging is emerging as an attractive method to quantify plaque burden and track changes in plaque longitudinally over time. However, plaque segmentation from US images is challenging because of poor boundary contrast and shadowing. The objective of this study is to develop and evaluate a semiautomatic segmentation algorithm with a novel stopping criterion for segmenting outer wall boundary (OWB) and lumen intima boundary (LIB) of common, internal, and external carotid artery from 3D US images for quantifying the vessel wall volume (VWV).
  
  
  METHODS
  3D US image volumes were acquired from ten subjects with asymptomatic carotid stenoses. Volumes were acquired using a mechanically scanned linear probe, and the reconstructed volume consisted of 21 slices acquired at an interslice distance of 1 mm. The authors used distance regularized level set method with edge-based energy, region-based energy, smoothness energy, and a novel stopping criterion to segment the LIB and OWB of carotid artery. The algorithm was initialized by six user-selected points on the LIB and OWB in seven 2D cross-sectional slices in each volume. An ellipse fitting and a stopping boundary-based energy is proposed to smooth the OWB contour and to stop leaking of the evolving contour, respectively. The algorithm was compared against ground truth boundaries generated from manual segmentations. The dice similarity coefficient (DSC), Hausdorff distance (HD), and modified HD (MHD) were used as error metrics.
  
  
  RESULTS
  The authors' proposed stopping boundary energy-based stopping criterion was compared with percentage change of area and change of the MHD between evolving contours at successive iterations stopping criteria. The performance of the proposed algorithm was better than other two stopping criteria and yielded mean of LIBDSC = 88.78%, OWBDSC = 94.81%, LIBMHD = 0.26 mm, OWBMHD = 0.25 mm, LIBHD = 0.74 mm, and OWBHD = 0.80 mm. The Bland-Altman plot and correlation coefficient (r = 0.99) indicated a high agreement between ground truth and algorithm-generated boundaries. The coefficient of variation (COV) and minimum detectable change of the VWV are 5.2% and 57.2 mm(3) (5.18% of mean VWV), calculated from repeated measurements of the VWV by algorithm. The mean absolute distance between corresponding points of the algorithm-generated and the ground truth boundaries was 0.25 mm.
  
  
  CONCLUSIONS
  The authors have developed a semiautomatic segmentation algorithm for measuring the VWV of the carotid artery using 3D US images with reduced operator interaction and computational time and higher reproducibility using a commercially available 3D US transducer. Their method is a step forward toward routine longitudinal monitoring of 3D plaque progression.
authors.affiliation: Department of Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030.
authors.forename: Md Murad
authors.initials: MM
authors.lastname: Hossain
authors.affiliation: Department of Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030.
authors.forename: Khalid
authors.initials: K
authors.lastname: AlMuhanna
authors.affiliation: School of Medicine, University of Maryland, Baltimore, Maryland 21201.
authors.forename: Limin
authors.initials: L
authors.lastname: Zhao
authors.affiliation: School of Medicine, University of Maryland, Baltimore, Maryland 21201 and Veterans Affairs Maryland Health Care System, Baltimore, Maryland 21201.
authors.forename: Brajesh K
authors.initials: BK
authors.lastname: Lal
authors.affiliation: Department of Bioengineering, George Mason University, Fairfax, Virginia 22030.
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Sikdar
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D058226:Plaque, Atherosclerotic / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D013997:Time Factors; D014463:Ultrasonography