Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility.
The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors’ knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images.
The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image.
The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.
The authors would like to acknowledge the comments from the anonymous reviews that greatly improved the quality of the paper. The authors thank A. D. Ward for the interesting discussions the authors had about the paper and S. Shavakh for generating the flattened VWT maps. The authors acknowledge the financial support from the Canadian Institutes of Health Research (CIHR) and the Ontario Research Fund (ORF) program. E. Ukwatta acknowledges the support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Graduate Scholarship (CGS). A. Fenster holds a Canada Research Chair in Biomedical Engineering, and acknowledges the support of the Canada Research Chair Program. G. Parraga holds a CIHR New Investigator award and gratefully acknowledges research funding and support from the CIHR.
I.A. Previous studies
II. MATERIALS AND METHODS
II.A. Algorithm initialization
II.B. 3D image preprocessing
II.C. Sparse field level set (SFLS) method
II.D. MAB segmentation
II.E. LIB segmentation
II.F.1. Evaluation metrics
II.G. Study subjects and imaging
III.A. Computational time and user interaction
IV.B. Computational time
IV.E. Comparison to previous methods
IV.F. Selection of proper ISD
- Medical imaging
- Medical image segmentation
- Vascular system
- Doppler ultrasonography
Data & Media loading...
Article metrics loading...
Full text loading...