Multi-model Fusion of Coronary CTA Segmentation Based on Attention Mechanism

Abstract

Coronary heart disease is one of the biggest health problems in the world, so the early prevention and diagnosis of coronary heart disease is very important. The presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. The detection of plaque and coronary artery segmentation have become the first choice for detecting coronary artery disease. At present, manual coronary artery segmentation is time-consuming and determined by the operator's subjective consciousness, which makes the need for automatic segmentation technology obvious in clinical diagnosis. In this paper, a method of computed tomography angiography (CTA) based on deep learning multi-model fusion is proposed, which includes three network models: an original 3D fully convolutional network (3D FCN) and two networks embedded the attention gate (AG) model in the original 3D FCN. And then the prediction results of the three networks are fused by majority voting algorithm to obtain the final result of the network prediction. In the post-processing stage, the level set function is used to further iteratively optimize the network fusion prediction results. In the process of evaluating the proposed method, Jaccard index (JI) and Dice similarity coefficient (DSC) scores are compared as performance measures. The final results show that the proposed method provides better and more accurate segmentation results.

Keywords

• coronary computed tomography angiography (cta)
• multi-model fusion
• three-dimensional fully convolutional network (3d fcn)
• attention gate model
• level set function