  X-ray coronary angiography (XCA) is a principal approach employed for
identifying coronary disorders. Deep learning-based networks have recently
shown tremendous promise in the diagnosis of coronary disorder from XCA scans.
A deep learning-based edge adaptive instance normalization style transfer
technique for segmenting the coronary arteries, is presented in this paper. The
proposed technique combines adaptive instance normalization style transfer with
the dense extreme inception network and convolution block attention module to
get the best artery segmentation performance. We tested the proposed method on
two publicly available XCA datasets, and achieved a segmentation accuracy of
0.9658 and Dice coefficient of 0.71. We believe that the proposed method shows
that the prediction can be completed in the fastest time with training on the
natural images, and can be reliably used to diagnose and detect coronary
disorders.
