Global Feature and Multi-level Feature Aggregation Segmentation Algorithm for Coronary

Abstract

Coronary computed tomographic angiography (CTA) image segmentation plays an important role in many practical applications, such as assisting doctors to judge vascular occlusion, vascular disease diagnosis, etc. In view of the large amount of noise in CTA images and not delicate segmentation results of traditional deep learning algorithms (including FCN, U-Net, V-Net, etc.), this paper proposes a global feature and multi-level feature aggregation network, which includes three network models, global feature module, feature fusion and refined V-shape multi-level feature aggregation module, and deep supervision module. The global feature module can filter the original CTA image and generate the basic features by integrating the early and later feature information and integrating the rich details and semantic information. The refined V-shape module generates refined feature maps of different levels on the basis of basic features, and obtains accurate coronary segmentation images by aggregating the refined feature maps of different levels. In addition, a deep supervision mechanism is added after each refined V-shape module to avoid the problem of gradient disappearing. The results show that the proposed method is superior to the mainstream baseline intuitively and quantitively. The ablation experiments also prove the effectiveness of each module.

Keywords

• coronary computed tomographic angiography (cta) segmentation
• global feature
• refined v-shape
• multi-level feature aggregation
• deep supervision