Coronary artery disease (CAD) has posed a leading threat to the lives of cardiovascular disease patients worldwide for a long time. Therefore, automated diagnosis of CAD has indispensable significance in clinical medicine. However, the complexity of coronary artery plaques that cause CAD makes the automatic detection of coronary artery stenosis in Coronary CT angiography (CCTA) a difficult task. In this paper, we propose a Transformer network (TR-Net) for the automatic detection of significant stenosis (i.e. luminal narrowing > 50%) while practically completing the computer-assisted diagnosis of CAD. The proposed TR-Net introduces a novel Transformer, and tightly combines convolutional layers and Transformer encoders, allowing their advantages to be demonstrated in the task. By analyzing semantic information sequences, TR-Net can fully understand the relationship between image information in each position of a multiplanar reformatted (MPR) image, and accurately detect significant stenosis based on both local and global information. We evaluate our TR-Net on a dataset of 76 patients from different patients annotated by experienced radiologists. Experimental results illustrate that our TR-Net has achieved better results in ACC (0.92), Spec (0.96), PPV (0.84), F1 (0.79) and MCC (0.74) indicators compared with the state-of-the-art methods. The source code is publicly available from the link (https://github.com/XinghuaMa/TR-Net).
翻译:长期以来,冠状动脉疾病(CAD)对全世界心血管疾病患者的生命构成了主要威胁。因此,对CAD的自动诊断在临床医学中具有不可或缺的意义。然而,导致CAD的冠状动脉红外形的复杂性使得冠状动脉动激素症的自动检测成为一项困难的任务。在本文件中,我们提议建立一个变压器网络(TR-Net),用于自动检测显著的神经紧张症(即光化缩小大于50%),同时实际完成计算机辅助的诊断。拟议的TR-Net引入了一个新的变压器,并紧密结合了卷动层和变动器编码,从而使其优势在任务中得到体现。通过分析语义信息序列,TR-Net能够充分理解多平流图图像(MPR)的每个位置的图像信息之间的关系,并根据当地和全球信息准确检测到显著的神经紧张症(即光线性缩小50%以上)。我们评估了我们的TR-Net网络的计算机辅助诊断。 TR-Net 新的变动图层和变压式的病人的实验结果,由不同实验的SLE-RISIF-MEMLS-MLS-S-S-C-C-S-S-SLA 做了有更好的实验结果。