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

Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder