Segmentation of the thoracic aorta using an attention-gated U-Net

Accurate segmentation of the aorta in computed tomography angiography (CTA) images is the first step for analysis of diseases such as aortic aneurysm, but manual segmentation can be prohibitively time-consuming and error prone. Convolutional neural network (CNN) based models have been utilized for automated segmentation of anatomy in CTA scans, with the ubiquitous U-Net being one of the most popular architectures. For many downstream image analysis tasks (e.g., registration, diameter measurement) very accurate segmentation accuracy may be required. In this work, we developed and tested a U-Net model with attention gating for segmentation of the thoracic aorta in clinical CTA data of patients with thoracic aortic aneurysm. Attention gating helps the model focus on difficult to segment target structures automatically and has been previously shown to increase segmentation accuracy in other applications. We trained U-Nets both with and without attention gating on 145 CTAs. Performance of the models were evaluated by calculating the DCS and Average Hausdorff Distance (AHD) on a test set of 20 CTAs. We found that the U-Net with attention gating yields more accurate segmentation than the U-Net without attention gating (DCS 0.966±0.028 vs. 0.944±0.022, AHD 0.189±0.134mm vs. 0.247±0.155mm). Furthermore, we explored the segmentation accuracy of this U-Net for multi-class labeling of various anatomic segments of the thoracic aorta, and found an average DCS of 0.86 for across 7 different labels. We conclude that the U-Net with attention gating improves segmentation performance and may aid segmentation tasks that require high levels of accuracy.