Download Research story

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.