Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic

BMC Medical Imaging

Jesper Alfraeus¹  Petter Dyverfeldt²  Mariana Bustamante²  Magnus Ziegler²  Elin Good³  Ebo de Muinck³  Jan Engvall⁴ 

[1] Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden;Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden;Department of Cardiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden;Department of Clinical Physiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden;

关键词: Atherosclerosis;    Carotid arteries;    Magnetic resonance imaging;    Contrast-enhanced;    Segmentation;    Deep learning;   

DOI  :  10.1186/s12880-021-00568-6

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY