The purpose of this study was to develop and validate a deep learning-based framework for automated segmentation and vessel shape analysis on non–contrast-enhanced magnetic resonance (MR) data of the thoracic aorta within the German National Cohort (GNC) MR study.
Materials and Methods:
One hundred data sets acquired in the GNC MR study were included (56 men, average age 53 y [22 to 72 y]). All participants had undergone non–contrast-enhanced MR imaging of the thoracic vessels. Automated vessel segmentation of the thoracic aorta was performed using a Convolutional Neural Network in a supervised setting with manually annotated data sets as the ground truth. Seventy data sets were used for training; 30 data sets were used for quantitative and qualitative evaluation. Automated shape analysis based on centerline extraction from segmentation masks was performed to derive a diameter profile of the vessel. For comparison, 2 radiologists measured vessel diameters manually.
Overall, automated aortic segmentation was successful, providing good qualitative analyses with only minor irregularities in 29 of 30 data sets. One data set with severe MR artifacts led to inadequate automated segmentation results. The mean Dice score of automated vessel segmentation was 0.85. Automated aortic diameter measurements were similar to manual measurements (average difference −0.9 mm, limits of agreement: −5.4 to 3.9 mm), with minor deviations in the order of the interreader agreement between the 2 radiologists (average difference −0.5 mm, limits of agreement: −5.8 to 4.8 mm).
Automated segmentation and shape analysis of the thoracic aorta is feasible with high accuracy on non–contrast-enhanced MR imaging using the proposed deep learning approach.