Key result
Active Shape Models for 3D ultrasound segmentation of the common carotid artery yielded a Dice Similarity Coefficient of 93.6% for lumen and 91.8% for adventitia compared to manual segmentation.
Why the study?
Does the Active Shape Models (ASMs) algorithm accurately and efficiently segment the common carotid artery from 3D ultrasound images compared to manual segmentation in patients with carotid stenosis?
Does the Active Shape Models (ASMs) algorithm accurately and efficiently segment the common carotid artery from 3D ultrasound images compared to manual segmentation in patients with carotid stenosis?
The proposed Active Shape Models algorithm provides fast and accurate segmentation of the common carotid artery from 3D ultrasound images, potentially facilitating clinical monitoring of atherosclerosis.
May support automated carotid segmentation research; leaves open clinical translation for atherosclerosis monitoring.
Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, we develop and evaluate a new segmentation method for outlining both lumen and adventitia (inner and outer walls) of common carotid artery (CCA) from three-dimensional ultrasound (3D US) images for carotid atherosclerosis diagnosis and evaluation. 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 80mg atorvastain and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. We investigate the use of Active Shape Models (ASMs) to segment CCA inner and outer walls after statin therapy. The proposed method was evaluated with respect to expert manually outlined boundaries as a surrogate for ground truth. For the lumen and adventitia segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 93.6%± 2.6%, 91.8%± 3.5%, mean absolute distances (MAD) of 0.28± 0.17mm and 0.34 ± 0.19mm, maximum absolute distances (MAXD) of 0.87 ± 0.37mm and 0.74 ± 0.49mm. The proposed algorithm took 4.4 ± 0.6min to segment a single 3D US images, compared to 11.7±1.2min for manual segmentation. Therefore, the method would promote the translation of carotid 3D US to clinical care for the fast, safety and economical monitoring of the atherosclerotic disease progression and regression during therapy.
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Yang et al. (2012) studied Carotid atherosclerosis (n=17). Active Shape Models (ASMs) segmentation vs. Manual segmentation was evaluated on Dice Similarity Coefficient (DSC) for lumen and adventitia segmentations. Active Shape Models for 3D ultrasound segmentation of the common carotid artery yielded a Dice Similarity Coefficient of 93.6% for lumen and 91.8% for adventitia compared to manual segmentation.