Abstract
Coronary artery calcification affects the arteries that supply the heart with blood, and percutaneous coronary intervention (PCI) is a direct and effective surgery to alleviate this symptom. In this paper, we propose a framework to judge if a patient requires surgery, based on cardiac computerized tomography scans. We adopt generative adversarial network to segment the calcified areas from slices. This architecture provides an environment for the generator to perform joint learning from ground truth images and the high-resolution discriminator. We use images reconstructed using two types of filters to test our method. An F1 score of 96.1% and 85.0% was achieved for the soft and sharp filters. In addition, we explored different recurrent neural networks for making the final decision. Including long short-term memory, which was ultimately used to deal with the calcium score normalized by the age and score threshold. Using the soft reconstruction image as the input, the whole framework achieved an accuracy of 76.6%. These results certify that our method can precisely locate lesion in artery, and make a reasonable risk assessment for PCI.
Highlights
The risk of coronary artery disease (CAD) in people has increased significantly, owing to factors such as irregular working hours, unhealthy diets, smoking, etc
The basic computerized tomography (CT) slice method with our algorithm is divided into three parts: segmentation with Patch generative adversarial network (GAN), Agatston score calculation based on segmentation results followed by score normalization, and the final judgement using Long short-term memory (LSTM)-fully connect (FC)
Each LSTM cell outputs a vector of length 64 and for the binary classification, the length of the vector reduces to two, After the Sigmoid layer, we get a probability from our algorithm indicating if the patient needs percutaneous coronary intervention (PCI)
Summary
The risk of coronary artery disease (CAD) in people has increased significantly, owing to factors such as irregular working hours, unhealthy diets, smoking, etc. Such a lifestyle is likely to cause tobacco addiction and diabetes. In the study of Hatmi et al [1], they used these two as an important indicator of CAD research. Atherosclerosis (AS) is the basic lesion of CAD, Libby et al [2] find it is an ongoing inflammatory response rather than a bland lipid storage disease. Frink et al [3] have explored the disease in depth 50 years ago
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