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Vessel stenosis is a major risk factor in cardiovascular diseases (CVD). Toanalyze 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 cardiologistsmay be a time-consuming task, and present a significant inter-observervariation. Although various computer-aided approaches have been developed tosupport segmentation of coronary arteries in CTA, the results remain unreliabledue to complex attenuation appearance of plaques, which are the cause of thestenosis. To overcome the difficulties caused by attenuation ambiguity, in thispaper, a 3D multi-channel U-Net architecture is proposed for fully automatic 3Dcoronary artery reconstruction from CTA. Other than using the original CTAimage, the main idea of the proposed approach is to incorporate the vesselnessmap into the input of the U-Net, which serves as the reinforcing information tohighlight the tubular structure of coronary arteries. The experimental resultsshow 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.

Coronary Artery Segmentation in Cardiac CT Angiography Using 3D Multi-Channel U-net