Fully Automated Deep Learning 3D Segmentation of the Aorta from Contrast Enhanced Magnetic Resonance Angiography Images

Motivation: Contrast-enhanced MRA (CE-MRA) of the thoracic aorta is an essential to assess and monitor aortic complications, and to quantify aortic dimensions. However, aortic dimensions’ measurement is cumbersome. Thus, automating aortic 3D-segmentation from CE-MRA is important to improve analysis workflow efficiency. Goal(s): We aimed to, accurately and precisely, automate thoracic aorta 3D-segmentation from CE-MRA scans using deep-learning. Approach: Using 125 CE-MRA scans we trained and tested a convolutional neural network to automatically segment the thoracic aortic. Results: Automated-segmentations was faster to output and had excellent agreement with manual-segmentations in metrics like aortic diameters and volume, dice scores, Hausdorff distance and average symmetrical surface distance. Impact: To our knowledge, this is the first study that implemented a fully-automated 3D-segmentation of contrast-enhanced MRA images. Such automation could possibly facilitate the clinical workflow when combined with future applications aiming at automating dimensions’ calculation at standardized locations.