基于半监督和多尺度级联注意力的超声颈动脉斑块分割方法

Ultrasound carotid plaque segmentation method based on semi-supervision and multi-scale cascaded attention

由于超声图像具有噪声强、质量低和边界模糊等特征,获取可靠的注释非常耗时费力,提出基于半监督和多尺度级联注意力的超声颈动脉斑块分割方法。首先,通过不确定性修正金字塔一致性(URPC)的半监督分割方法充分利用未标记数据训练模型减轻费时费力的标注压力。其次,提出一种基于边缘检测的双编码器结构,并利用边缘检测编码器辅助超声斑块图像特征编码器充分获取边缘信息;另外,设计了一个多尺度融合模块(MSFM),通过自适应融合多尺度特征改善提取不规则形状斑块的结果,并结合一个级联通道空间注意力(CCSA)模块更好地关注斑块区域;最后,在超声颈动脉斑块图像数据集上评估所提方法。实验结果表明,所提方法在该数据集上的Dice指标和交并比(IoU)指标比监督方法CA-Net(Comprehensive Attention convolutional neural Network)分别提升了约2.8和6.3个百分点,比半监督方法循环原型一致性学习(CPCL)分别提高了约1.8和1.3个百分点,所提方法可以有效提高超声颈动脉斑块图像的分割准确度。

Obtaining reliable labels is time-consuming and laborious caused by the characteristics of ultrasonic images such as strong noise,low quality and blurred boundary.Therefore,a semi-supervision and multi-scale cascaded attention based ultrasound carotid plaque segmentation method was proposed.Firstly,a semi-supervised segmentation method of Uncertainty Rectified Pyramid Consistency(URPC)was used to make full use of unlabeled data to train the model,so as to reduce the time-consuming and laborious labeling pressure.Then,a dual encoder structure based on edge detection was proposed,and the edge detection encoder was used to assist the ultrasonic plaque image feature encoder to fully acquire the edge information.In addition,a Multi-Scale Fusion Module(MSFM)was designed to improve the extraction of irregularly shaped plaques by adaptive fusion of multi-scale features,and a Cascaded Channel Spatial Attention(CCSA)module was combined to better focus on the plaque region.Finally,the proposed method was evaluated on the ultrasonic carotid plaque image dataset.Experimental results show that the Dice index and IoU(Intersection over Union)index of the proposed method on the dataset are 2.8 and 6.3 percentage points higher than those of the supervised method CA-Net(Comprehensive Attention convolutional neural Network)respectively,and 1.8 and 1.3 percentage points higher than those of the semisupervised method Cyclic Prototype Consistency Learning(CPCL)respectively.It can be seen that this method can effectively improve the segmentation accuracy of ultrasound carotid plaque image.