# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 1 Introduction  
Coronary heart disease, in which lifestyle and environmental factors are risk factors, is one of the leading cardiovascular diseases affecting the global population [1]. Coronary angiography has been used clinically for decades as the \"gold standard\" for the diagnosis of coronary heart disease [2]. Invasive coronary angiography (ICA) is an important method for arterial stenosis detection and is widely used in clinical treatment. Typically, in ICAs, a radiopaque contrast agent is injected into the coronary arteries, and then the arteries are viewed with the use of consecutive X-rays. In clinical practice, the diagnosis decision usually considers the percent stenosis as one of the judgment factors of severity, which is determined by a physician's visual assessment. For example, 50% stenosis is generally considered the minimum criterion for coronary revascularization, and 70% stenosis is considered an indicator for identifying clinical lesions. However, this method has some limitations, such as reliance on professionally trained radiologists and judgment errors due to fatigue and other reasons. On the other hand, limitations in imaging modalities, suchas very low contrast caused by a limited radiation dose or illumination and imaging blur caused by a limited number of viewing angles and diluted contrast agents, also make the accuracy of the manual determination of coronary stenosis challenging [3]. These unsolved problems have been developed with the outstanding contributions of deep learning in the field of image processing. Recently, the deep learning methods represented by convolutional neural networks (CNNs) have made it possible to locate stenosis and calculate the stenosis rate more accurately, stably, and reliably. Within the existing body of literature, diligent scholars have undertaken extensive research endeavors encompassing various facets of this complex domain. Some have delved into the intricacies of blood vessel extraction [4], while others have harnessed the power of deep learning methods to precisely locate stenotic lesions [5]. Concurrently, certain researchers have proposed novel mathematical approaches to calculate stenosis rates [6]. Although these pioneering studies have significantly contributed to the field, they often exhibit a predominant focus on stenotic areas within the aorta, inadvertently overlooking the critical implications of stenosis within branch vessels. This oversight can be attributed to challenges stemming from overlapping vessels, inadequate contrast agent distribution, and suboptimal imaging angles, which frequently hinder the comprehensive visualization of branch vessels. Consequently, the detection of stenosis within complete coronary angiography presents an intriguing and formidable research frontier, characterized by abundant opportunities and intricate challenges that await exploration. Addressing this critical aspect holds the potential to substantially augment our understanding and capabilities in diagnosing and managing stenotic conditions within the intricate network of coronary arteries. To address the formidable challenges posed by the intricate network of coronary arteries, characterized by numerous branches and complex geometries, an efficient approach involves leveraging Hessian matrix multiscale filtering for blood vessel enhancement [7]. This method capitalizes on the inherent correlations among the eigenvalues of the Hessian matrix to identify and extract the tubular structural characteristics of blood vessels within medical images. Notably, the Hessian-based Frangi vesselness (HFV) filter has gained widespread prominence for vascular enhancement and quantification, as evidenced by its utilization in various studies [8; 9]. In addition to standalone approaches, hybrid enhancement techniques have also emerged, such as the amalgamation of region growth with the HFV filter [10], aimed at further refining image backgrounds and suppressing noise.