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.
翻译:在现代世界,冠心病(CHD)是造成死亡的主要原因,发展用于诊断和治疗CHD的现代分析工具正受到科学界的极大关注。深层次的基于学习的算法,如分解网络和探测器,通过及时分析病人的血管图解,在帮助医疗专业人员方面发挥了重要作用。本文侧重于X-光冠心血管动脉学(XCA),这被认为是诊断和治疗CHD的“黄金标准 ” 。首先,我们描述了可公开获得的 XCA 图像数据集。然后,对图像预处理的古典和现代技术进行了审查。此外,还讨论了共同框架选择技术,这是投入质量和模型性能的一个重要因素。在接下来的两章中,我们讨论了现代船舶分解和缓冲检测网络,最后是当前工艺的开放问题和当前局限性。