Deep Learning Based Automatic Segmentation of the Thoracic Aorta from Chest Computed Tomography in Healthy Korean Adults

This study aimed to establish reference values for aortic size using a fully automated deep learning-based segmentation method on chest computed tomography (CT) scans. The study included 704 healthy Korean adults (mean age 50.6 years; 57.8% males) who underwent contrast-enhanced chest CT for health screening. A convolutional neural network (CNN) was trained and applied to 3D CT images to automatically segment the thoracic aorta based on the Society for Vascular Surgery/Society of Thoracic Surgeons classification. The CNN-generated masks were reviewed and corrected by expert cardiac radiologists. Results showed that aortic size was significantly larger in males across all zones (zones 0-8) and increased with age, approximately 1 mm per decade. For example, aortic size in zone 2 increased from 25.4 mm in men aged 30-39 to 29.8 mm in men aged 70 and older. The deep learning algorithm provided reliable aortic measurements, which have clinical implications for detecting aortic aneurysms and other aortic diseases. These research findings were used in the development of aview.