  The segmentation and classification of carotid plaques in ultrasound images
play important roles in the treatment of atherosclerosis and assessment for the
risk of stroke. Although deep learning methods have been used for carotid
plaque segmentation and classification, two-stage methods will increase the
complexity of the overall analysis and the existing multi-task methods ignored
the relationship between the segmentation and classification. These will lead
to suboptimal performance as valuable information might not be fully leveraged
across all tasks. Therefore, we propose a multi-task learning framework
(RCCM-Net) for ultrasound carotid plaque segmentation and classification, which
utilizes a region confidence module (RCM) and a sample category confidence
module (CCM) to exploit the correlation between these two tasks. The RCM
provides knowledge from the probability of plaque regions to the classification
task, while the CCM is designed to learn the categorical sample weight for the
segmentation task. A total of 1270 2D ultrasound images of carotid plaques were
collected from Zhongnan Hospital (Wuhan, China) for our experiments. The
results showed that the proposed method can improve both segmentation and
classification performance compared to existing single-task networks (i.e.,
SegNet, Deeplabv3+, UNet++, EfficientNet, Res2Net, RepVGG, DPN) and multi-task
algorithms (i.e., HRNet, MTANet), with an accuracy of 85.82% for classification
and a Dice-similarity-coefficient of 84.92% for segmentation. In the ablation
study, the results demonstrated that both the designed RCM and CCM were
beneficial in improving the network's performance. Therefore, we believe that
the proposed method could be useful for carotid plaque analysis in clinical
trials and practice.
