Main aortic segmentation from CTA with deep feature aggregation network

In this study, we propose a Deep Feature Aggregation network (DFA-Net) for main aortic segmentation from CTA (Computed Tomography Angiography) by aggregating features from forwarding layers to Ieverage more visual information. To practically verify the effectiveness of our method, we collect 90 CTA volumes from Beijing AnZhen Hospital up to over 60 thousands 2-D slices. First, we use a level-set based algorithm to efficiently generate the dataset for training and validating the deep model. Then the dataset is divided into three parts, 70 instances are used for training and 5 instances are used for validating the best parameters, and the rest 15 instances are used for testing the generalization of the model. Finally, the testing result shows that mIoU (mean Intersection-over-Union) of the segmentation result is 0.943, which indicates that by properly aggregating more visual features in a deep network the segmentation model can achieve state-of-the-art performance.