论文标题
使用卷积神经网络从计算机断层扫描中检测肺栓塞
Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network
论文作者
论文摘要
肺栓塞（PE）的临床症状非常多样化和非特异性，这使得难以诊断。此外，肺栓塞具有多个触发因素，是血管死亡的主要原因之一。因此，如果可以快速检测并治疗，它可以大大降低住院患者死亡的风险。在检测过程中，计算机断层扫描肺血管造影（CTPA）的成本很高，血管造影需要注射造影剂，这增加了对患者损害的风险。因此，本研究将使用深度学习方法来检测所有使用卷积神经网络拍摄胸部CT图像的患者的肺栓塞。通过提出的肺栓塞检测系统，我们可以在患者的第一个CT图像的同时检测到肺栓塞的可能性，并立即安排CTPA测试，节省超过一周的CT图像筛查时间并为患者提供及时的诊断和治疗。
The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learning approach to detect pulmonary embolism in all patients who take a CT image of the chest using a convolutional neural network. With the proposed pulmonary embolism detection system, we can detect the possibility of pulmonary embolism at the same time as the patient's first CT image, and schedule the CTPA test immediately, saving more than a week of CT image screening time and providing timely diagnosis and treatment to the patient.
