# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 3 Experiments and Results
### Results  
We separately evaluated and compared the effects of preprocessing with an HFV filter and image fusion with the original image on the performance of the deep learning network for stenosis detection. Following the completion of image preprocessing, we embarked on the crucial task of annotating the positions of stenosis within the main and branch vessels, specifically targeting stenosis rates exceeding 50%, in accordance with the diagnostic findings provided by medical professionals. For visual clarity and reference, we present Figure 1(a1-a3) within our manuscript, each illustrating a distinct aspect of our analysis. Figure 1(a1) showcases the original, unprocessed image, providing a baseline for our observations. In contrast, Figure 1(a2) displays the image subjected to preprocessing using the HFV filter, revealing the enhancements achieved through this technique. Lastly, Figure 1(a3) presents the image after undergoing image fusion, illustrating the outcome of integrating the preprocessed image with its original counterpart. These visual representations serve as valuable aids in elucidating the effects of our image processing methods and the localization of stenosis positions within the vascular structures under examination. To establish a reference benchmark for subsequent experiments, we initiated our investigation by evaluating the performance of the original images across various neural network architectures. This comprehensive assessment encompassed a range of state-of-the-art models, including YOLOv4, FasterRCNN-ResNet 101, FasterRCNN-Inceptionresnetv2, R-FCN-ResNet 101, and R-FCN-Inceptionresnetv2. Our objective was to quantify the efficacy of each model by calculating the mAP at varying IoU thresholds within the spectrum of 0.1 to 0.6. Figure 2 serves as a graphical representation of the performance of these diverse neural networks when applied to our dataset, thus providing a visual elucidation of their comparative strengths and weaknesses in the context of stenosis detection. Among these neural network models, YOLOv4 exhibited mAP values of 0.2788 and 0.2125 at IoU thresholds of 0.1 and 0.6, respectively. In contrast, R-FCN-Inseptionresnetv2 demonstrated a more competitive performance, displaying mAP values of 0.5615 and 0.4456 at the corresponding IoU thresholds of 0.1 and 0.6. Notably, it is apparent from Figure 3 that both of these models exhibit limitations in effectively identifying stenotic lesions present within branch vessels, pointing towards a specific challenge in their performance in this context. Figure 1: Images of left coronary artery (LCA) and right coronary artery (RCA) at zero imaging angle: (**a1,b1**) are the original images of LCA and RCA, respectively; (**a2,b2**) are the images enhanced using Hessian-based Frangi vesselness filter; and (**a3,b3**) are the images after image fusion. Figure 2: Network performance on the dataset without HFV filtering. We compared the preprocessed images with the original images at the next six imaging angles. These images clearly showed that the vascular features in the images, especially the main vessels, were enhanced by applying the HFV filter. At the same time, the noise that appeared in the background was partially eliminated. For example, Figure 1(a2) shows the improved appearance of the tubular structures. Nevertheless, we also observed some pseudo-vessels that resembled the shape of the branch vessels throughout the image. Although these structures appeared realistic, they were present in areas where there were no vessels or where the vessels were blurred in the original image. A similar effect is shown in Figure 1(b2), where the HFV filter enhances the features while weakening the fine vessels in the background that are blurred due to the low amount of contrast. In our quest to optimize the enhancement of all vascular features within the images while preserving fine vascular details to the greatest extent possible, we contemplated the utilization of image fusion as a potential solution.