肺结节智能检测和三维可视化系统设计与实现

Design and Realization of Intelligent Detection and Three-dimensional Visualization System for Pulmonary Nodules

为了提高肺部疾病识别效率,减少肺结节漏诊率,设计了一套肺结节智能检测和三维可视化系统;构建一个基于RESNET的深度多通道三维卷积神经网络,根据LUNA16公开数据集的888例患者图像,选择权重参数为α=0.5,γ=2的Focal loss损失函数进行训练,在CT图像上对可疑的肺结节进行检测,采用光线投射算法对检测出的结节区域进行体绘制三维重建;经实验测试,该网络与单通道网络和特征金字塔网络(Feature Pyramid network,FPN)相比,准确度最高,为84.8%,系统能够在230 s内自动检测肺结节并完成三维重建,对于分辨率1 mm/pixel的CT图像灵敏度在98%以上,用户可在浏览器上查看结节检测结果和三维重建模型;该系统突破了终端设备和地域限制,能够为肺部疾病提供辅助诊断,提高诊断效率。

In order to improve the recognition efficiency of lung diseases and reduce the rate of missed diagnosis of lung nodules,a set of intelligent detection and three-dimensional visualization system of lung nodules was designed.Methods:A deep multi-channel three-dimensional convolutional neural network based on RESNET was constructed.Based on the 888 patient images of the LUNA16 public data set,a Focal loss loss function withα=0.5 andγ=2 was selected for training.The suspicious lung nodules are detected,and the ray projection algorithm is used to perform volume rendering three-dimensional reconstruction of the detected nodules.Results:After experimental tests,the network has the highest accuracy compared with the single-channel network and Feature Pyramid network(FPN),which is 84.8%.The system can automatically detect lung nodules and complete 3D reconstruction within 230 s.The sensitivity of CT images with a resolution of 1mm/pixel is above 98%.Users can view the nodule detection results and 3D reconstruction models on the browser.Conclusion:The system breaks through the limitation of terminal equipment and area,and can provide auxiliary diagnosis for lung diseases and improve the diagnosis efficiency.