# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 1 Introduction
[13] proposed a deep learning-based workflow for stenosis detection and severity classification, and [PERSON] et al. [14] combined three models with various architectures to raise the accuracy. [PERSON] [15] applied quantum computing in the context of a hybrid transfer-learning paradigm for stenosis detection to improve the performance of a pretrained network. Additionally, [PERSON] [16] applied CycleGAN to simulate angiograms for the purpose of augmenting datasets. Furthermore, a novel approach known as dynamic coronary road mapping with deep learning-based Bayesian filtering has been reported to improve visual feedback and reduce the use of contrast during PCI [17]. [PERSON] [18] used an RGB-CNN model for SPECT myocardial perfusion imaging to diagnose CAD and compared it with pretrained VGG-16 and DenseNet-121 networks. [PERSON] [19] investigated a deep-learning algorithm for the quantification of coronary artery calcium scores based on computed tomography data from 530 patients. Actually, the most common application of deep learning in CAG is coronary artery segmentation. [PERSON] [20] proposed a convolutional neural network (CNN) approach for detecting vessel regions in angiography images. With the development of a fully convolutional network (FCN) and U-Net, deep learning shows good performance in coronary artery segmentation. [PERSON] et al. [21] proposed a robust method for major vessel segmentation that could maintain high connectivity in most narrow areas. Later, they introduced a penalty term for penalizing false negatives and false positives into the dice coefficient of the loss function to improve performance [22]. [PERSON] [23] evaluated an end-to-end U-Net-inspired deep learning model for the segmentation and quantification of cardiac structures in coronary computed tomography angiography. [PERSON] [24] presented a framework for a new segmentation model for leukocyte images using an extreme learning machine, which can assist in diagnosing various diseases, such as leukemia, malaria, psoriasis, and AIDS. In order to further stimulate the potential of deep learning in segmentation, more and more research has combined different machine learning techniques into deep learning. [PERSON] [25] applied the gradient boosting decision tree (GBDT) and deep forest classifiers into CAG segmentation, while [PERSON] [26] proposed an adaptive instance normalization style transfer technique for segmenting the coronary arteries. However, most works focus on improving the overall accuracy of segmentation instead of emphasizing the edge information of the coronary artery, which just ignores one of the most crucial factors for accurate QCA computation and thus cannot be competent in the process of QCA. Additionally, previous works [21; 22; 27], in this field, primarily focused on the segmentation of main vessels, including the left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA), but neglected their primary branches. These branches, defined as vessels greater than 1 mm, are also clinically significant and important for QCA. Differently, we focus on the segmentation and quantification of not only the primary coronary arteries, including LAD, LCX, and RCA, but also their primary branches, where edge information about coronary arteries is also well considered. Overall, all the above literature have made significant contributions, but the studies on automatic QCA are very few. Only several studies have attempted to incorporate deep learning techniques into QCA to improve its accuracy and efficiency. [PERSON] [28] proposed an automatic QCA method using a U-Net segmentation model. However, the QCA workflow was not entirely automated, as the stenosis lesion areas were manually selected and the segmentation results required manual correction. [PERSON] [29] integrated a feature pyramid with a U-Net++ model for automatic extraction and stenosis evaluation.