Research And Implementation Of Deep Learning Based Algorithm For Coronary Artery Calcification Lesions Detection

Nowadays,coronary artery-related diseases have become one of the main causes of death from disease.Coronary artery calcification(CAC)is highly correlated with the degree of atherosclerosis,and atherosclerosis could lead to a majority of coronary heart diseases.The detection of calcified plagues in coronary arteries can evaluate the risks of future cardiac events.Coronary computed tomographic angiography(CCTA)images are different from computer tomography(CT)images in that lesions.They are in low contrast to surroundings and hard to be identified.And CAC detection is different from general object detection tasks.Because the images are lack of color information.It is also the difficulty of this study.The main contributions of this thesis could be summarized in three parts as follows.A novel neural network for coronary calcified lesion detection based on coronary angiography images is designed and implemented,which can independently detect coronary calcified lesions in the original coronary angiography images without significant gray-scale differences while ensuring the detection speed.The multi-scale feature fusion module and the feature fine extraction module are designed to enhance the feature richness and semantic information.The information from the input contrast images is effectively utilized to enhance the information sharing among local features,to further localize and focus the features of lesions with coronary calcification,and to fuse features with different resolutions as well as semantic information of different intensities to achieve feature complementarity among different layers.The proposed CFE structure is further optimized by adding supervision to the inter-pixel correlation of features in it,which enhances the correlation of features within each object frame.A coronary angiography dataset is constructed,consisting of 3,070 DICOM medical coronary angiography images,each of which is selected by a professional doctor with key frames and labeled with the location and type of calcified lesions,and selected 2,470 images randomly as training set,and the rest of the images automatically constitute the test set.Multiple experiments are designed in this thesis to verify the efficacy of the Self-designed feature extraction modules.Ultimately,both the qualitative clinical medicine and the quantitative analysis of experimental results demonstrate the innovation and validity of this thesis’s coronary artery calcified lesion detection network in its application.