一种抗金属伪影干扰的双流自注意力分割网络

An anti-metal artifact interference dual-stream self-attention segmentation network

金属伪影导致计算机断层扫描(computed tomography,CT)影像质量降低,针对金属伪影严重干扰CT影像分割结果的问题,提出了一种抗金属伪影干扰的分割网络。该网络采用复合连接的双流编码器结构,双主干分别对受干扰和未受干扰的CT影像进行特征提取。复合连接结构整合了两条主干上编码器提取的特征。设计了基于Transformer的焦点自注意力(self-attention,SA)机制模块提升网络对全局多尺度信息的编码能力。混合损失和辅助监督被用于优化网络的训练过程。实验表明,该网络在金属伪影干扰下分割结果的平均Dice系数、MIoU和Recall分别为86.40%,93.11%和90.76%。该网络在面向CT影像语义分割时具有很好的抗金属伪影干扰效果,无需伪影去除的情况下可获得较高的分割精度。

Metal artifact leads to reduced quality of computed tomography(CT)images,which can severely degrade segmentation accuracy.To address this problem,a segmentation network is proposed to resist metal artifact interference.This network uses composite connected dual-stream encoder structure with two backbones for feature extraction from disturbed and undisturbed CT images,respectively.The composite connection structure integrates the features extracted by the encoders on the two backbones.A Transformer-based focal self-attention(SA)mechanism block is developed to encode global multi-scale information.The training process of the network is optimized using hybrid loss and ancillary supervision.The experimental results show that this network on metal artifact data could reach 86.40%,93.11%and 90.76%in average Dice coefficient,MIoU and Recall,respectively.The network has great anti-metal artifact interference effect in semantic segmentation for CT images,and achieves high segmentation accuracy without artifact reduction.