论文标题
通过计算机断层扫描肺血管造影检测早期肺栓塞检测的卷积神经网络
Convolutional Neural Network for Early Pulmonary Embolism Detection via Computed Tomography Pulmonary Angiography
论文作者
论文摘要
进行了这项研究，以开发一种计算机辅助检测系统（CAD）系统，用于对患有肺栓塞（PE）的患者进行分类。该系统的目的是在等待期内降低死亡率。计算机断层扫描肺血管造影（CTPA）用于PE诊断。由于CTPA报告要求放射科医生审查此案并提出进一步的管理，因此这创建了一个等待期，患者可能会死亡。因此，我们提出的CAD方法旨在从没有PE的患者的PE分类患者。与涉及识别关键PE病变图像以加快PE诊断的CAD系统的相关研究相反，我们的系统包括一个新型的分类模型集合，用于PE检测和用于PE病变标记的分割模型。这些模型是使用国家郑项大学医院和开放资源的数据进行了培训的。对于接收器工作特性曲线（精度= 0.85），分类模型产生了0.73，而分割模型的平均交点为0.689。提出的CAD系统可以区分有和没有PE的患者并自动标记PE病变以加快PE诊断
This study was conducted to develop a computer-aided detection (CAD) system for triaging patients with pulmonary embolism (PE). The purpose of the system was to reduce the death rate during the waiting period. Computed tomography pulmonary angiography (CTPA) is used for PE diagnosis. Because CTPA reports require a radiologist to review the case and suggest further management, this creates a waiting period during which patients may die. Our proposed CAD method was thus designed to triage patients with PE from those without PE. In contrast to related studies involving CAD systems that identify key PE lesion images to expedite PE diagnosis, our system comprises a novel classification-model ensemble for PE detection and a segmentation model for PE lesion labeling. The models were trained using data from National Cheng Kung University Hospital and open resources. The classification model yielded 0.73 for receiver operating characteristic curve (accuracy = 0.85), while the mean intersection over union was 0.689 for the segmentation model. The proposed CAD system can distinguish between patients with and without PE and automatically label PE lesions to expedite PE diagnosis
