  Vessel stenosis is a major risk factor in cardiovascular diseases (CVD). To
analyze the degree of vessel stenosis for supporting the treatment management,
extraction of coronary artery area from Computed Tomographic Angiography (CTA)
is regarded as a key procedure. However, manual segmentation by cardiologists
may be a time-consuming task, and present a significant inter-observer
variation. Although various computer-aided approaches have been developed to
support segmentation of coronary arteries in CTA, the results remain unreliable
due to complex attenuation appearance of plaques, which are the cause of the
stenosis. To overcome the difficulties caused by attenuation ambiguity, in this
paper, a 3D multi-channel U-Net architecture is proposed for fully automatic 3D
coronary artery reconstruction from CTA. Other than using the original CTA
image, the main idea of the proposed approach is to incorporate the vesselness
map into the input of the U-Net, which serves as the reinforcing information to
highlight the tubular structure of coronary arteries. The experimental results
show that the proposed approach could achieve a Dice Similarity Coefficient
(DSC) of 0.8 in comparison to around 0.6 attained by previous CNN approaches.
