Key result
An Active Shape Model-based segmentation method for 3D ultrasound images of the common carotid artery achieved high accuracy (Dice Similarity Coefficient >92%) and reduced segmentation time to 4.3 minutes compared to 11.7 minutes manually.
Why the study?
Does an Active Shape Model (ASM) based segmentation method accurately and efficiently segment the common carotid artery on 3D ultrasound compared to manual expert segmentation?
Does an Active Shape Model (ASM) based segmentation method accurately and efficiently segment the common carotid artery on 3D ultrasound compared to manual expert segmentation?
An ASM-based segmentation method provides accurate and significantly faster segmentation of the common carotid artery on 3D ultrasound compared to manual tracing, potentially facilitating clinical monitoring of atherosclerosis.
Supports automated carotid segmentation on 3D ultrasound; leaves open prospective validation before clinical adoption.
Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.
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Yang et al. (2013) studied Carotid atherosclerosis (n=17). Active Shape Model (ASM) based segmentation method vs. Manual segmentation was evaluated on Segmentation accuracy (Dice Similarity Coefficient) and processing time. An Active Shape Model-based segmentation method for 3D ultrasound images of the common carotid artery achieved high accuracy (Dice Similarity Coefficient >92%) and reduced segmentation time to 4.3 minutes compared to 11.7 minutes manually.
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