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.

中文翻译：

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通过正弦图域中的深度学习改进计算机辅助检测肺结节。

肺结节的计算机辅助检测（CADe）在协助放射科医生诊断和减轻肺癌解释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程序，建立在计算机断层扫描 (CT) 图像上，并具有特征提取功能，用于检测和诊断。CT 图像中的人类视觉感知是由正弦图重建的，它是从 CT 扫描仪获取的原始原始数据。在这项工作中，与传统的基于图像的 CADe 系统不同，我们提出了一种新的基于正弦图的 CADe 系统，其中使用完整的投影信息来探索正弦图域中结节的其他有效特征。面对这一概念研究有限、正弦图领域未知有效特征的挑战，我们设计了一个新的 CADe 系统，它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。所提议的系统在来自公开可用的在线肺图像数据库联盟数据库的 208 例患者病例中得到验证，每个病例至少有一个胸膜旁结节注释。实验结果表明，我们提出的方法仅基于正弦图获得了 0.91 的接收器操作特征曲线下面积 (AUC) 值，而仅基于 CT 图像的值为 0.89。此外，正弦图和CT图像的组合可以进一步将AUC的值提高到0.92。该研究表明，通过深度学习，在正弦图域中检测肺结节是可行的。