基于序列注意力和局部相位引导的骨超声图像分割网络

Bone Ultrasound Segmentation Network Based on Sequential Attention and Local Phase Guidance

在超声辅助的骨科手术导航中,需要从采集的超声图像序列中精确分割出骨结构,并展示给医生,来辅助医生进行术中决策.但是,图像噪声、成像伪影以及模糊的骨边界导致从超声图像序列中精确分割提取骨结构十分困难.为解决该问题,提出一种新的基于序列注意力与局部相位引导的骨超声图像分割网络.该网络一方面自适应地利用超声序列帧之间的关系即序列注意力来辅助骨结构的语义分割.另一方面,该网络通过引入局部相位引导模块,突出骨边缘信息,进一步提高分割精度.利用包含19050幅图像的骨超声数据集,进行交叉实验、消融实验并与最新的超声骨分割方法进行比较.实验结果表明所提方法对骨结构分割精度高,优于现有的超声骨分割方法.

In the ultrasound assisted navigation of orthopaedics,the bone structure needs to be segmented accurately from the collected ultrasound images and displayed to the doctor to assist the intraoperative decision-making.However,it is difficult to segment bone structures from ultrasound images because of imaging noises,shadow artifacts and blurred bone boundaries.For solving this problem,this paper proposes a bone ultrasound image segmentation network based on sequential attention and local phase guidance.On the one hand,the network adaptively uses the relationship between frames of ultrasound sequence,that is,sequence attention,to assist the semantic segmentation of bone structures.On the other hand,the local phase guidance module is introduced to highlight the bone edge information and further improve the segmentation accuracy.We performed the cross validation,ablation experiments and the comparison experiments with the state-of-arts by using a dataset that contained 19050 bone ultrasound images.The experimental results show that the proposed method has high accuracy and is superior to the existing bone segmentation methods.