Segmentation of intravascular ultrasound images by mask propagation network

Intravascular ultrasound (IVUS) image is widely used in coronary atherosclerotic plaque analysis. The delineation of coronary lumen borders and external elastic lamina (EEL) in IVUS images is a crucial step in the analysis. Conventional segmentation approaches by delineating each image frame independently would lead to incorrect segmentation when guide wires, side branches, and calcified plaques presented. In this work, we proposed a new framework via mask propagation method for the segmentation of IVUS image sequences by using information from previous frame and current frame to predict the mask probability map. Experiments showed that our proposed method achieved an encouraging result with Dice Similarity Coefficient (Dice) of 0.973 and 0.949, Jaccard Index (JI) of 0.949 and 0.906, and Hausdorff Distance (HD) of 0.187 mm and 0.225 mm for delineation of EEL and lumen, respectively.