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Name: Li Guanyu Student ID: 018082910044 A review of coronary artery segmentation techniques in medical image Nowadays, coronary heart disease has become a serious public health concern issue. Computer aided diagnosis (CAD) in coronary quantitative measurement and functional evaluation benefits medical professionals for the diagnosis and treatment of coronary diseases. The accuracy of coronary artery segmentation plays a vital role in CAD system. A growing number of automatic and semi-automatic vessel segment methods have been developed in the last 30 years. Frangi et al. proposed a vesselness filter [1] with a Hessian matrix, which has been widely used for the vessel segmentation in medical image. Based on Frangi’s vesselness filter, Yang et al. developed an automatic coronary artery extraction method [2] in 2012. This method is totally a traditional method and can be proved mathematically. At the same year, Kitamura et al. introduced a novel segment method [3], which combines the traditional method and machine learning. It should be noted that the traditional method plays the major role in Kitamura’s method. With the development of machine learning, Wolterink et al. reported a coronary artery segment method [4], which is designed totally through machine learning algorithms and performs well. In the last 5 years, no great method like Frangi filter is developed in vessel segmentation. Pure traditional method has reached its bottlenecks for coronary artery segmentation. According to that, more research focus on machine learning method, which has more potential to improve the accuracy. With the development of GPU and data density growing, it can be expected that machine learning will play role a more important role in vessel segmentation. References [1] Frangi A F , Niessen W J , Vincken K L , et al. Multiscale vessel enhancement filtering[J]. 1998. [2] Yang G , Kitslaar P , Frenay M , et al. Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography[J]. The International Journal of Cardiovascular Imaging, 2012, 28(4):921-933. [3] Kitamura Y , Li Y , Ito W . Automatic coronary extraction by supervised detection and shape matching.[C]. IEEE International Symposium on Biomedical Imaging. IEEE, 2012. [4] Wolterink J M , Van Hamersvelt R W , Viergever M A , et al. Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier[J]. 2018.