Gao Yongfeng, Tan Jiaxing, Liang Zhengrong, Li Lihong, Huo Yumei
Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA.
Departments of Computer Science, City University of New York/CSI, Staten Island, NY, 10314, USA.
Vis Comput Ind Biomed Art. 2019 Nov 22;2(1):15. doi: 10.1186/s42492-019-0029-2.
Computer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists' diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists' examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.
肺结节的计算机辅助检测（CADe）在辅助放射科医生诊断和减轻肺癌解读负担方面发挥着重要作用。当前的CADe系统旨在模拟放射科医生的检查流程，基于计算机断层扫描（CT）图像构建，通过特征提取进行检测和诊断。CT图像中的人类视觉感知是从正弦图重建而来的，正弦图是从CT扫描仪获取的原始数据。在这项工作中，与传统的基于图像的CADe系统不同，我们提出了一种新颖的基于正弦图的CADe系统，其中利用完整的投影信息在正弦图域中探索结节的其他有效特征。面对这一概念研究有限以及正弦图域中有效特征未知的挑战，我们设计了一种新的CADe系统，该系统利用卷积神经网络的自学习能力从正弦图中学习和提取有效特征。所提出的系统在公开可用的在线肺部图像数据库联盟数据库中的208例患者病例上进行了验证，每个病例至少有一个胸膜旁结节标注。实验结果表明，我们提出的方法仅基于正弦图的接收器操作特征曲线下面积（AUC）值为0.91，而仅基于CT图像的AUC值为0.89。此外，正弦图和CT图像的组合可进一步将AUC值提高到0.92。这项研究表明，利用深度学习在正弦图域中进行肺结节检测是可行的。
