The completely automated multiresolution edge snapper (CAMES) achieved a figure-of-merit of 95.8% compared with 87.4% for CALEX, improving overall IMT measurement bias by 36%.
Does the CAMES technique improve the accuracy of carotid ultrasound IMT measurement compared to manual segmentation and the CALEX technique?
The CAMES technique provides an automated, accurate, and robust method for carotid IMT measurement, outperforming a previous automated technique.
Tasa de eventos absoluta: 95.8% vs 87.4%
The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.
Molinari et al. (Wed,) conducted a other in Carotid artery intima-media thickness measurement (n=365). Completely automated multiresolution edge snapper (CAMES) vs. CALEX (feature-based extraction and classifier) was evaluated on Figure-of-merit for automated carotid artery recognition and segmentation. The completely automated multiresolution edge snapper (CAMES) achieved a figure-of-merit of 95.8% compared with 87.4% for CALEX, improving overall IMT measurement bias by 36%.
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