基于边界显著性的超声颈动脉内中膜的智能提取

Automatic extraction of carotid intima-media by ultrasound based on boundary saliency

为进一步提高超声颈动脉内中膜提取和测量的准确性,本研究基于U-Net模型提出了改进的分割网络,以实现对颈动脉内中膜的精准提取。首先,在网络中加入条形注意力模块,利用先验形状和解剖信息以解决传统卷积感受野受限的问题;此外,结合后处理细化模块以更好地减少图像中噪声和伪影干扰,通过从内中膜的固有膜形状特征中学习,从而实现校正估计误差。在采集的1000张颈动脉血管超声图像数据库中进行测试,分割Dice达到0.932,内中膜厚度的平均误差为0.914个像素。本研究有望为动脉疾病的自动分析提供重要的参考依据。

In order to further improve the accuracy of intima-media extraction and measurement by ultrasound,we proposed an improved segmentation network based on U-Net model to achieve accurate extraction of carotid intima-media.Firstly,the stripe attention module was added to the network to solve the problem of traditional convolutional restricted receptive field by using prior shape and anatomical information.In addition,by combining the post processing refinement module.The interference of noise and artifacts in the image was reduced better,and the estimation error was corrected by learning from the intrinsic film shape features of the inner and middle film.The test was carried out in the database of 1000 carotid artery ultrasound images collected.The segmentation Dice reached 0.932,and the average error of the thickness of the inner and media membranes was 0.914 pixels.This research is expected to provide important reference value for the automatic analysis of arterial diseases.