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Intravascular ultrasound (IVUS) is the preferred modality for capturingreal-time and high resolution cross-sectional images of the coronary arteries,and evaluating the stenosis. Accurate and real-time segmentation of IVUS imagesinvolves the delineation of lumen and external elastic membrane borders. Inthis paper, we propose a two-stream framework for efficient segmentation of 60MHz high resolution IVUS images. It combines shallow and deep networks, namely,CSDN. The shallow network with thick channels focuses to extract low-leveldetails. The deep network with thin channels takes charge of learninghigh-level semantics. Treating the above information separately enableslearning a model to achieve high accuracy and high efficiency for accuratereal-time segmentation. To further improve the segmentation performance, mutualguided fusion module is used to enhance and fuse both different types offeature representation. The experimental results show that our CSDNaccomplishes a good trade-off between analysis speed and segmentation accuracy.

CSDN: Combing Shallow and Deep Networks for Accurate Real-time Segmentation of High-definition Intravascular Ultrasound Images