The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. Nowadays, CTPA images are the gold standard computerized detection method to determine and identify the symptoms of pulmonary embolism (PE), although performing CTPA is harmful for patients and also expensive. Therefore, we aim to detect possible PE patients through CT images. The system will simulate CTPA images with deep learning models for the identification of PE patients' symptoms, providing physicians with another reference for determining PE patients. In this study, the simulated CTPA image generation system uses a generative antagonistic network to enhance the features of pulmonary vessels in the CT images to strengthen the reference value of the images and provide a basis for hospitals to judge PE patients. We used the CT images of 22 patients from National Cheng Kung University Hospital and the corresponding CTPA images as the training data for the task of simulating CTPA images and generated them using two sets of generative countermeasure networks. This study is expected to propose a new approach to the clinical diagnosis of pulmonary embolism, in which a deep learning network is used to assist in the complex screening process and to review the generated simulated CTPA images, allowing physicians to assess whether a patient needs to undergo detailed testing for CTPA, improving the speed of detection of pulmonary embolism and significantly reducing the number of undetected patients.
翻译:这项研究的目的是开发一个系统,在临床上为肺栓塞栓塞诊断而生成模拟计算成的心血管血管血管血管成像(CTPA),现在,CTPA图像是确定和识别肺栓塞(PE)症状的金质标准计算机化检测方法,尽管执行CTPA对病人有害,而且费用也很高。因此,我们的目标是通过CT图像检测可能的PE病人。该系统将模拟CTPA图像,并用深入学习模型模拟PE病人症状,为医生提供确定PE病人的另一种参考。在本研究中,模拟CTPA图像生成系统使用一种基因化对抗网络,加强CT图像中肺浆容器的特征,以加强图像的参考价值,为医院判断PE病人提供依据。我们用国家成功大学医院22名病人的CT图像和相应的CTPA图像作为培训数据,用于模拟CTPA的不耐久的图像,并用两套基因分析速度网络生成这些图像。预计,在临床诊断过程中,将大量使用CLIVA测试方法来改进CVA的临床测试。