Abstract
BackgroundNon-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to validate and demonstrate a method for segmenting the carotid bifurcation into the common, internal, and external carotid arteries (CCA, ICA, ECA) in contrast-enhanced MR angiography (CE-MRA) data.MethodsA segmentation pipeline utilizing a convolutional neural network (DeepMedic) was tailored and trained for multi-class segmentation of the carotid arteries in CE-MRA data from the Swedish CardioPulmonsary bioImage Study (SCAPIS). Segmentation quality was quantitatively assessed using the Dice similarity coefficient (DSC), Matthews Correlation Coefficient (MCC), F2, F0.5, and True Positive Ratio (TPR). Segmentations were also assessed qualitatively, by three observers using visual inspection. Finally, geometric descriptions of the carotid bifurcations were generated for each subject to demonstrate the utility of the proposed segmentation method.ResultsBranch-level segmentations scored DSC = 0.80 ± 0.13, MCC = 0.80 ± 0.12, F2 = 0.82 ± 0.14, F0.5 = 0.78 ± 0.13, and TPR = 0.84 ± 0.16, on average in a testing cohort of 46 carotid bifurcations. Qualitatively, 61% of segmentations were judged to be usable for analyses without adjustments in a cohort of 336 carotid bifurcations without ground-truth. Carotid artery geometry showed wide variation within the whole cohort, with CCA diameter 8.6 ± 1.1 mm, ICA 7.5 ± 1.4 mm, ECA 5.7 ± 1.0 mm and bifurcation angle 41 ± 21°.ConclusionThe proposed segmentation method automatically generates branch-level segmentations of the carotid arteries that are suitable for use in further analyses and help enable large-cohort investigations.
Highlights
Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses
Non-invasive imaging is of interest for tracking the progression of Ziegler et al BMC Med Imaging (2021) 21:38 atherosclerosis in the carotid arteries and has the potential to be used for risk stratification or treatment decisions [1]
No statistically significant differences were found between whole-bifurcation and branch-specific segmentation quality scores for Dice similarity coefficient (DSC), Matthews Correlation Coefficient (MCC), F 2, and F 0.5
Summary
Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. Non-invasive imaging is of interest for tracking the progression of Ziegler et al BMC Med Imaging (2021) 21:38 atherosclerosis in the carotid arteries and has the potential to be used for risk stratification or treatment decisions [1]. In this realm, magnetic resonance imaging (MRI) presents several opportunities. To extract this information, the vessels must be identified and delineated from the images When performed manually, this is a difficult and time-consuming process and the amount of time required increases with further localization, for example, when segmenting each arterial branch, i.e. internal (ICA), external (ECA), and common carotid arteries (CCA). Inter- and intra-observer variability decreases the consistency of segmentations and suggests an area where automated approaches could improve on current practice [9]
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