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Carotid arteries vulnerable plaques are a crucial factor in the screening ofatherosclerosis by ultrasound technique. However, the plaques are contaminatedby various noises such as artifact, speckle noise, and manual segmentation maybe time-consuming. This paper proposes an automatic convolutional neuralnetwork (CNN) method for plaque segmentation in carotid ultrasound images usinga small dataset. First, a parallel network with three independent scaledecoders is utilized as our base segmentation network, pyramid dilationconvolutions are used to enlarge receptive fields in the three segmentationsub-networks. Subsequently, the three decoders are merged to be rectified inchannels by SENet. Thirdly, in test stage, the initially segmented plaque isrefined by the max contour morphology post-processing to obtain the finalplaque. Moreover, three loss function Dice loss, SSIM loss and cross-entropyloss are compared to segment plaques. Test results show that the proposedmethod with dice loss function yields a Dice value of 0.820, an IoU of 0.701,Acc of 0.969, and modified Hausdorff distance (MHD) of 1.43 for 30 vulnerablecases of plaques, it outperforms some of the conventional CNN-based methods onthese metrics. Additionally, we apply an ablation experiment to show thevalidity of each proposed module. Our study provides some reference for similarresearches and may be useful in actual applications for plaque segmentation ofultrasound carotid arteries.

Parallel Network with Channel Attention and Post-Processing for Carotid Arteries Vulnerable Plaque Segmentation in Ultrasound Images