Research On Segmentation Of Coronary Artery Angiography Images Based On Deep Learning

Coronary heart disease is the "first killer" of human health,and its morbidity and mortality rates are increasing year by year,posing a serious threat to human health.Coronary angiography is the gold standard for coronary artery disease diagnosis.The analysis and study of coronary angiography images play an important role in the prevention and diagnosis of coronary heart disease.Extracting blood vessels from coronary angiography images is helpful to physicians in analyzing conditions and in subsequent modeling efforts for vascular data analysis.Since manual annotation is highly subjective and labour-intensive,there is an urgent need for automatic extraction algorithms based on computer technology.Traditional algorithms are computationally intensive and its segmentation results are rough,while the method based on deep learning has the advantages of fast computation,high accuracy and robustness,so it is more suitable for the task of vessel segmentation of coronary angiography images with blurred illumination and severe noise.Many scholars at home and abroad have done a lot of fruitful work in the field of vessel segmentation based on deep learning,but the segmentation performance of most networks is limited because of the blurred illumination and low image quality of coronary angiography images,resulting in frequent loss of fine vessels,vessel disconnection and noise in the segmentation results.Based on the above problems,this paper proposes coronary angiography image segmentation methods based on residual convolution and local enhanced spatial attention,respectively.The main research is as follows:(1)Aiming at the problems of dim and blurred light of coronary angiography images and the loss of fine vessels in the segmentation results of most existing segmentation methods,this paper proposes a coronary artery segmentation strategy based on the fusion of residual convolution features.First,this paper analyzes the characteristics of coronary angiography image segmentation task and improves a low light image enhancement algorithm.By optimizing the image edge weights to enhance the contrast between the foreground and background of blood vessels,the image is more conducive to the recognition of targets by the subsequent network.Then,in order to enhance the representation capability of the segmentation network for the features of fine vessels and improve the segmentation performance of the network for fuzzy fine vessels,a pair of residual convolutional feature fusion modules are proposed in this paper to improve the aggregation capability of the network for semantic information.The experimental results show that the coronary artery segmentation method proposed in this paper can segment more fine vessels and effectively avoid the phenomenon of fine vessel loss in the segmentation results.(2)For the vascular breakage problem that appears in the segmentation results,this paper proposes a new local enhanced spatial attention(LESA)mechanism based on the existing spatial attention.Placing LESA in the middle of the encoding path and decoding path deepens the connection between the encoded and the decoded.Due to the fusion of features in different directions,LESA can obtain more spatial information while acquiring channel information of coronary angiography images,better grasp the precise location of coronary vessels,enhance the long-distance dependence between coronary vessel pixel features,and make the segmentation network more focused on the segmentation target,thus effectively avoiding the phenomenon of vascular rupture in the segmentation results.The experimental results show that the method proposed in this paper can well solve the vascular breakage problem in the segmentation results.