An improved approach for accurate and efficient measurement of common carotid artery intima-media thickness in ultrasound images

BSMNet: Boundary-salience multi-branch network for intima-media identification in carotid ultrasound images

Carotid artery intima-media thickness (CIMT) is an essential factor in signaling the risk of cardiovascular diseases, which is commonly evaluated using ultrasound imaging. However, automatic intima-media segmentation and thickness measurement are still challenging due to the boundary ambiguity of intima-media and inherent speckle noises in ultrasound images. In this work, we propose an end-to-end boundary-salience multi-branch network, BSMNet, to tackle the carotid intima-media identification from ultrasound images, where the prior shape knowledge and anatomical dependence are exploited using a parallel linear structure learning modules followed by a boundary refinement module. Moreover, we design a strip attention model to boost the thin strip region segmentation with shape priors, in which an anisotropic kernel shape captures long-range global relations and scrutinizes meaningful local salient contexts simultaneously. Extensive experimental results on an in-house carotid ultrasound (US) dataset demonstrate the promising performance of our method, which achieves about 0.02 improvement in Dice and HD95 than other state-of-the-art methods. Our method is promising in advancing the analysis of systemic arterial disease with ultrasound imaging.

Evaluation Of Atherosclerosis Severity Based On Carotid Artery Intima-Media Thickness Changes: A New Diagnostic Criterion

This study aimed to identify instant intima-media thickness changes (ΔIMT) in the common carotid artery (CCA) during cardiac cycle in order to assess atherosclerosis progression. Using a computerized semi-automated method, instant IMT changes were extracted in the two walls of the left CCA (240 consecutive patients) using B-mode ultrasound images. We found that CCA ΔIMT increased from 8 ± 4% of IMT_max in the controls to 15 ± 6% of IMT_max in the severe stenosis group. According to the multiple ordinal regression analysis, ΔIMT was associated with the severity of carotid artery stenosis (odds ratio [OR], 4.95; p < 0.001), independent of sex (OR, 1.11; p = 0.04), age (OR, 1.14; p < 0.001), body mass index; OR, 1.13; p = 0.036), hypertension (OR, 2.04; p < 0.001), diabetes (OR, 1.38; p = 0.045) and hyperlipidemia (OR, 1.54; p = 0.002). We concluded that increment of CCA ΔIMT during the cardiac cycle was strongly and independently associated with severity of carotid artery stenosis or atherosclerosis progression.

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).