Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images

Abstract

In vivo MRI provides a means to non-invasively image and assess the morphological features of atherosclerotic carotid arteries. To assess quantitatively the degree of vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. Currently this is done manually, which is time-consuming and sensitive to inter- and intra-observer variability. The goal of this work was to develop an automated contour detection technique for tracing the lumen, outer boundary and plaque contours in carotid MR short-axis black-blood images. Seventeen patients with carotid atherosclerosis were imaged using high-resolution in vivo MRI, generating a total of 50 PD- and T1-weighted MR images. These images were automatically segmented using the algorithm presented in this work, which combines model-based segmentation and fuzzy clustering to detect the vessel wall, lumen and lipid core boundaries. The results demonstrate excellent correspondence between automatic and manual area measurements for lumen (r = 0.92) and outer (r = 0.91), and acceptable correspondence for fibrous cap thickness (r = 0.71). Though further optimization is required, our algorithm is a powerful tool for automatic detection of lumen and outer boundaries, and characterization of plaque in atherosclerotic vessels.

Exploring individual user differences in the 2D/3D interaction with medical image data

“…In many pathologies, a limited number of quantitative parameters describe the relevant clinical findings from the imaging study. Like any image processing system, automated segmentation algorithms can produce mistakes, e.g., when the contrast level or the amount of noise differs from those specified or when the bifurcations or closely located vessels are misinterpreted by the algorithm and as such affect the segmentation result (Adame et al 2004). To obtain correct measurements, manual adjustments or overwrites to automated segmentation results are frequently needed in routine clinical practice.…”

Assessment of the carotid artery by MRI at 3T: A study on reproducibility

“…We analyzed all of the images using the VesselMASS software package developed at our institution (23). This software package allows the semiautomated detection of the luminal and outer wall boundaries of the vessel wall from MR images, and the subsequent derivation of various quantitative parameters describing the vessel wall.…”

MRI of atherosclerosis in clinical trials

“…Combinations of such techniques are beginning to automatically yield the desired in vivo characterizations of plaque tissue. For example, Adame et al recently showed the potential to automatically characterize the fibrous cap based on segmentation of contrast-enhanced images (97).…”