A Review of Modern Approaches for Coronary Angiography Imaging Analysis
It addresses the need for improved diagnostic tools for Coronary Heart Disease, a leading cause of death, but is incremental as it is a review paper.
This paper reviews modern deep learning approaches for analyzing X-Ray Coronary Angiography images to assist in diagnosing and treating Coronary Heart Disease, summarizing datasets, preprocessing techniques, segmentation, and stenosis detection networks.
Coronary Heart Disease (CHD) is a leading cause of death in the modern world. The development of modern analytical tools for diagnostics and treatment of CHD is receiving substantial attention from the scientific community. Deep learning-based algorithms, such as segmentation networks and detectors, play an important role in assisting medical professionals by providing timely analysis of a patient's angiograms. This paper focuses on X-Ray Coronary Angiography (XCA), which is considered to be a "gold standard" in the diagnosis and treatment of CHD. First, we describe publicly available datasets of XCA images. Then, classical and modern techniques of image preprocessing are reviewed. In addition, common frame selection techniques are discussed, which are an important factor of input quality and thus model performance. In the following two chapters we discuss modern vessel segmentation and stenosis detection networks and, finally, open problems and current limitations of the current state-of-the-art.