A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification

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.