Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

• Published in: Medical physics (Lancaster) Vol. 47; no. 3; pp. 1034 - 1047
• Main Authors: Ruijter, Joerik, Sambeek, Marc, Vosse, Frans, Lopata, Richard
• Format: Journal Article
• Language: English
• Published: United States John Wiley and Sons Inc 01.03.2020
• Subjects: automated segmentation; Automation; carotid artery bifurcation; Carotid Artery Diseases - diagnostic imaging; Carotid Artery Diseases - pathology; Carotid Stenosis - diagnostic imaging; Carotid Stenosis - pathology; Case-Control Studies; geometry assessment; Humans; image analysis; Imaging, Three-Dimensional - methods; QUANTITATIVE IMAGING AND IMAGE PROCESSING; Ultrasonography - instrumentation; ultrasound; geometry assessment; image analysis; automated segmentation; carotid artery bifurcation; ultrasound
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.13960

Purpose Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient‐specific three‐dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high‐resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high‐resolution two‐dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view. Methods A slow sweep (v = ± 5 mm/s) was made over the subject’s neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star‐Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high‐pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall‐lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen–plaque boundary, Otsu’s automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient‐specific solid mechanics model of the complete arterial wall. Results The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images . Conclusions With this method, a subject‐specific biomechanical model can be constructed directly from a hand‐held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B‐mode images in both healthy and diseased arteries.