# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 4 Discussion  
In this study, we present a new automated stenosis analysis method called AI-QCA, which utilizes deep segmentation models. The proposed AI-QCA achieved several goals, as follows: Firstly, AI-QCA achieves fully automatic stenosis analysis and generates quantitative parameters, including DS, MLD, RVD, and LL, without any manual operation in the process. Secondly, according to the low values of RMSE, the generated quantitative parameters are in high agreement with the QCA results operated by experts. Thirdly, the AI-QCA significantly reduces the time cost of stenosis analysis, from 20 min with a manual QCA to just 8 s with AI-QCA. For segmentation, Figure 6 shows the intuitive visual results of BasNet, Pix2 Pix, U\({}^{2}\)-Net, and U-Net. BasNet and U\({}^{2}\)-Net have better performance in connectivity, while U-Net sometimes leads to the disconnection of vessel branches. More branch lacking occurred in U\({}^{2}\)-Net, and Pix2 Pix may cause more small redundant branches. Overall, BasNet shows the best segmentation performance, which is closer to ground truth. The results of Tables 1-4 interpret the statements above. Except for BasNet, Pix2 Pix behaves better in recall, while U\({}^{2}\)-Net behaves better in precision. For vessel types, Pix2 Pix  
Figure 12: An example of AI-QCA and QCA on RCA. (**a**) displays the results of a QCA fitting and analysis, while (**b**) displays the results of an AI-QCA fitting and analysis. (**c**) and (**d**), respectively, display the fitting diameter with reference fitting lines of QCA and AI-QCA. In (**c**), the proximal, ostial, and distal locations are indicated by the labels “\(p\)”, “\(o\)”, and “\(d\)”. In (**d**), these locations are indicated by vertical lines in pink, dark red, and green, respectively. QCA = quantitative coronary analysis; RCA = right coronary artery; DS = diameter stenosis; MLD = minimal luminal diameter; RVD = reference vessel diameter; and LL = lesion length. behaves better in terms of LAD and RCA than U\({}^{2}\)-Net, while it is just the opposite for LCX. The underlying reason is that Pix2 Pix is an image-to-image translation method based on generative adversarial networks (GAN), which tends to segment more vessels, contributing to higher recall, but it also comes with more small redundant branches. U\({}^{2}\)-Net is a deep segmentation approach with nested U-structure using inverted residual block and dual-channel attention mechanism, which concentrates more on improving the precision, causing the occasional loss of vessel branches. While BasNet takes advantage of multiscale feature fusion mechanisms and handles different levels of saliency, it has better segmentation performance. In the evaluation of CAT, the average distance variances from BasNet are obviously lower than those of Pix2 Pix, according to Table 5. This is because BASNet is specifically designed to be boundary-aware, whereas the points on the CAT represent the average of the vessel edges. It can be deduced from Figure 8 that distance variances increase along the CAT. The distance variances of the main vessels are relatively low, but tend to increase when it comes to the branches, regardless of the type of vessel. The distance variances of the main vessels are similar for BasNet and Pix2 Pix because the segmentation performances are both excellent. However, BasNet outperforms Pix2 Pix in the segmentation of branches, as seen in the lower distance variances when the points on the CAT are far from the root node. In the QCA evaluation, DS, MLD, RVD, and LL generated by AI-QCA showed agreement with those of QCA operated by experts, as shown by the RMSE values in Figure 9. At the same time, we can see that the Pearson coefficients of DS are better than MLD, RVD, and LL. Additionally, a small amount of MLD, RVD, and LL in AI-QCA are higher than those in QCA. The first reason may be attributed to the errors with the scales caused by converting pixels to millimeters as defined by the segmentation of catheter width.