PURPOSE: To develop a semiautomatic method based on level set method (LSM) for carotid arterial wall thickness (CAWT) measurement. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) of diseased carotid arteries was acquired from 10 patients. Ground truth (GT) data for arterial wall segmentation was collected from three experienced vascular clinicians. The semiautomatic variational LSM was employed to segment lumen and arterial wall outer boundaries on 102 MR images. Two computer-based measurements, arterial wall thickness (WT) and arterial wall area (AWA), were computed and compared with GT. Linear regression, Bland-Altman, and bias correlation analysis on WT and AWA were applied for evaluating the performance of the semiautomatic method. RESULTS: Arterial wall thickness measured by radial distance measure (RDM) and polyline distance measure (PDM) correlated well between GT and variational LSM (r = 0.83 for RDM and r = 0.64 for PDM, P < 0.05). The absolute arterial wall area bias between LSM and three observers is less than 10%, suggesting LSM can segment arterial wall well compared with manual tracings. The Jaccard Similarity (Js ) analysis showed a good agreement for the segmentation results between proposed method and GT (Js 0.71 ± 0.08), the mean curve distance for lumen boundary is 0.34 ± 0.2 mm between the proposed method and GT, and 0.47 ± 0.2 mm for outer wall boundary. CONCLUSION: The proposed LSM can generate reasonably accurate lumen and outer wall boundaries compared to manual segmentation, and can work similar to a human reader. However, it tends to overestimate CAWT and AWA compared to the manual segmentation for arterial wall with small area.
PURPOSE: To develop a semiautomatic method based on level set method (LSM) for carotid arterial wall thickness (CAWT) measurement. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) of diseased carotid arteries was acquired from 10 patients. Ground truth (GT) data for arterial wall segmentation was collected from three experienced vascular clinicians. The semiautomatic variational LSM was employed to segment lumen and arterial wall outer boundaries on 102 MR images. Two computer-based measurements, arterial wall thickness (WT) and arterial wall area (AWA), were computed and compared with GT. Linear regression, Bland-Altman, and bias correlation analysis on WT and AWA were applied for evaluating the performance of the semiautomatic method. RESULTS: Arterial wall thickness measured by radial distance measure (RDM) and polyline distance measure (PDM) correlated well between GT and variational LSM (r = 0.83 for RDM and r = 0.64 for PDM, P < 0.05). The absolute arterial wall area bias between LSM and three observers is less than 10%, suggesting LSM can segment arterial wall well compared with manual tracings. The Jaccard Similarity (Js ) analysis showed a good agreement for the segmentation results between proposed method and GT (Js 0.71 ± 0.08), the mean curve distance for lumen boundary is 0.34 ± 0.2 mm between the proposed method and GT, and 0.47 ± 0.2 mm for outer wall boundary. CONCLUSION: The proposed LSM can generate reasonably accurate lumen and outer wall boundaries compared to manual segmentation, and can work similar to a human reader. However, it tends to overestimate CAWT and AWA compared to the manual segmentation for arterial wall with small area.
Authors: Pankaj K Jain; Neeraj Sharma; Luca Saba; Kosmas I Paraskevas; Mandeep K Kalra; Amer Johri; John R Laird; Andrew N Nicolaides; Jasjit S Suri Journal: Diagnostics (Basel) Date: 2021-12-02