KEYWORDS: Image segmentation, Arteries, Education and training, Magnetic resonance imaging, Data modeling, Independent component analysis, Simulation of CCA and DLA aggregates, 3D image processing, Scanners, Performance modeling
PurposeAtherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential.ApproachWe propose an automatic segmentation of the carotid artery in cross-sections perpendicular to the centerline to make the segmentation invariant to the image plane orientation and allow a correct assessment of the vessel wall thickness (VWT). We trained a residual U-Net on eight sparsely sampled cross-sections per carotid artery and evaluated if the model can segment areas that are not represented in the training data. We used 218 MRI datasets of 121 subjects that show hypertension and plaque in the ICA or CCA measuring ≥1.5 mm in ultrasound.ResultsThe model achieves a high mean Dice coefficient of 0.948/0.859 for the vessel’s lumen/wall, a low mean Hausdorff distance of 0.417/0.660 mm, and a low mean average contour distance of 0.094/0.119 mm on the test set. The model reaches similar results for regions of the carotid artery that are not incorporated in the training set and on MRI of young, healthy subjects. The model also achieves a low median Hausdorff distance of 0.437/0.552 mm on the 2021 Carotid Artery Vessel Wall Segmentation Challenge test set.ConclusionsThe proposed method can reduce the effort for carotid artery vessel wall assessment. Together with human supervision, it can be used for clinical applications, as it allows a reliable measurement of the VWT for different patient demographics and MRI acquisition settings.
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.
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