  Coronary heart disease is one of the top rank leading cause of mortality in
the world which can be because of plaque burden inside the arteries.
Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging
technology which captures the real time and high resolution images of the
coronary arteries and can be used for the analysis of these plaques. The IVUS
segmentation involves the extraction of two arterial walls components namely,
lumen and media. In this paper, we investi- gate the effectiveness of
Convolutional Neural Networks including U-Net to segment ultrasound scans of
arteries. In particular, the proposed seg- mentation network was built based on
the the U-Net with the VGG16 encoder. Experiments were done for evaluating the
proposed segmen- tation architecture which show promising quantitative and
qualitative results.
