  Carotid arteries vulnerable plaques are a crucial factor in the screening of
atherosclerosis by ultrasound technique. However, the plaques are contaminated
by various noises such as artifact, speckle noise, and manual segmentation may
be time-consuming. This paper proposes an automatic convolutional neural
network (CNN) method for plaque segmentation in carotid ultrasound images using
a small dataset. First, a parallel network with three independent scale
decoders is utilized as our base segmentation network, pyramid dilation
convolutions are used to enlarge receptive fields in the three segmentation
sub-networks. Subsequently, the three decoders are merged to be rectified in
channels by SENet. Thirdly, in test stage, the initially segmented plaque is
refined by the max contour morphology post-processing to obtain the final
plaque. Moreover, three loss function Dice loss, SSIM loss and cross-entropy
loss are compared to segment plaques. Test results show that the proposed
method 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 vulnerable
cases of plaques, it outperforms some of the conventional CNN-based methods on
these metrics. Additionally, we apply an ablation experiment to show the
validity of each proposed module. Our study provides some reference for similar
researches and may be useful in actual applications for plaque segmentation of
ultrasound carotid arteries.
