Atherosclerosis of the carotid artery is a major risk factor for stroke. Current studies analyze cross-sections of 3D MR black-blood images to assess the vessel wall of carotid arteries. To increase the reproducibility of quantitative biomarkers such as vessel wall thickness and radiomic features, a reliable automatic segmentation of the vessel wall in these cross-sections is essential. CNN-based segmentation is well established and has been successfully applied for 2D vessel wall and plaque segmentation. We trained a residual U-Net on sparsely sampled cross-sections that are perpendicular to the vessel’s centerline, making our method invariant to the image plane orientation. Due to the well curated training data and the usage of the vessel’s centerline as anatomical prior we are able to achieve a high mean Dice coefficient of 0.946/0.864 for the vessel’s lumen/wall and low mean average contour distance of 0.100/0.116 mm. To prove the model’s flexibility, we show that it is able to segment regions of the carotid artery that are not incorporated in the training data, achieving a similar Dice coefficient, average contour distance and Hausdorff distance. This validates the potential of the method in accurately automating carotid artery wall segmentation for any vessel cross-section. The model is also evaluated on young, healthy subjects and the 2021 Carotid Artery Vessel Wall Segmentation Challenge test set, proving its versatility.
KEYWORDS: Image segmentation, Independent component analysis, Tissues, Simulation of CCA and DLA aggregates, Arteries, Visualization, Image processing algorithms and systems, Angiography, Computed tomography, Image visualization
Measurements of the vessel lumen diameter are often used to determine the degree of atherosclerotic disease in carotid arteries. However, quantification results vary with imaging technique and acquisition settings. We aim at providing a tool that quantifies the lumen diameter on different image datasets and gives an estimate of quantification uncertainties, so that they can be taken into consideration when evaluating and comparing measurements. For the segmentation of the vessel lumen, we present an algorithm using ray-casting techniques and partial volume correction. We furthermore propose a scheme for the analysis and exploration of the lumen diameter. Finally, we present a clinically relevant application scenario, in which we explore agreement between lumen diameter estimations in corresponding computed tomography angiography, contrast-enhanced magnetic resonance angiography, time-of-flight, and subtraction images of carotid vessels with severe carotid atherosclerotic plaques.
KEYWORDS: Image segmentation, Tissues, Arteries, 3D image enhancement, Acquisition tracking and pointing, Image processing algorithms and systems, Visualization, 3D acquisition, Digital imaging
Measurements of the vessel lumen diameter are often used to determine the degree of atherosclerotic disease in carotid arteries. However, quantification results vary with imaging technique and acquisition settings. In this work, we aim at providing a tool, that quantifies the lumen diameter on different image datasets and gives an estimate of quantification uncertainties, so that they can be taken into consideration when evaluating and comparing measurements. For the segmentation of the vessel lumen we present an algorithm using ray-casting techniques and partial volume correction. We furthermore propose a scheme for analysis and exploration of the lumen diameter. Finally, we present clinically relevant application scenario, in which we explore agreement between lumen diameter estimations in corresponding CTA, CEMRA, TOF and subtraction images of carotid vessels with severe carotid atherosclerotic plaques.
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