心肌灌注靶心图缺血缺失程度分割方法

Segmentation method of myocardial perfusion bull-eye for the degree of loss of cardiac ischemia

心肌灌注显像作为无创影像学技术之一,为冠心病心肌缺血的诊断提供依据。本文针对心肌灌注显像图中的靶心图,基于U型网络(U-Net)提出包含多层转置卷积上采样拼接模块和四通路可加权通道注意力模块的分支结构,并将分支结构的输出结果与主干U-Net的输出结果进行融合,实现心肌灌注靶心图心脏缺血缺失程度部位的精确分割。实验结果表明:多层转置卷积上采样拼接模块实现了不同深度特征图的融合,有效地降低与缺失程度相似的重度稀疏程度对分割的干扰。四通路可加权通道注意力模块能进一步提高两种相似程度的区分能力及对目标边缘细节的学习能力,保留更丰富的边缘细节特征。本文所用实验数据来自天津医科大学总医院、天津泰达医院、天津第一、第三中心医院数据库,在自建数据集上雅卡尔(Jaccard)系数较U-Net提高5.00%。研究结果表明,本文模型优于目前基于U-Net进行优化的其他模型,主观评价满足临床诊断的精度要求。

As one of the non-invasive imaging techniques,myocardial perfusion imaging provides a basis for the diagnosis of myocardial ischemia in coronary heart disease.Aiming at the bull-eye image in myocardial perfusion imaging,this paper proposed a branching structure,which included multi-layer transposed convolution up-sampling concatenate module and four-channel weighted channels attention module,and the output results of the branch structure were fused with the output results of trunk U-Net,to achieve accurate segmentation of the cardiac ischemia missing degree in myocardial perfusion bull-eye image.The experimental results show that the multi-layer transposed convolution up-sampling concatenate module realizes the fusion of different depth feature maps,and effectively reduces the interference of the severe sparse degree which is similar to the missing degree on the segmentation.Four-channel weighted attention module can further improve the ability to distinguish between the two similar degrees and the ability to learn edge details of the targets,and retain more abundant edge details features.The experimental data came from Tianjin Medical University General Hospital,Tianjin TEDA Hospital,Tianjin First Central Hospital and Third Central Hospital.The Jaccard scores in the self-built dataset was 5.00%higher than that of U-Net.The model presented in this paper is superior to other optimized models based on U-Net,and the subjective evaluation meets the accuracy requirements for clinical diagnosis.