Intelligent Assessment of Percutaneous Coronary Intervention Based on GAN and LSTM Models

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.INDEX TERMS Generative adversarial network, low-dose cardiac CT, recurrent neural network, percutaneous coronary intervention, coronary calcium scoring.

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

“…Because of these artifacts, blood vessel extraction from X-ray images must be effective. The semantic segmentation of coronary vessels is extremely important in the clinical process of cardiology analysis ( Zou et al, 2020 ). Both left coronary artery (LCA) and left anterior descending (LAD) artery segmentation with stenosis are tedious and time-consuming in the existing methods, as depicted in Fig.…”