Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

• Published in: Visual computing for industry, biomedicine and art Vol. 2; no. 1; pp. 15 - 9
• Main Authors: Gao, Yongfeng, Tan, Jiaxing, Liang, Zhengrong, Li, Lihong, Huo, Yumei
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 22-11-2019; Springer Nature B.V; SpringerOpen
• Subjects: Annotations; Artificial neural networks; CAE) and Design; Computed tomography; Computer Graphics; Computer Imaging; Computer Science; Computer simulation; Computer-aided detection; Computer-Aided Engineering (CAD; Consortia; Data acquisition; Deep learning; Diagnosis; Domains; Feature extraction; Image acquisition; Image detection; Image Processing and Computer Vision; Image reconstruction; Lung; Machine learning; Media Design; Medical imaging; Medical Imaging Modeling; Nodules; Original; Original Article; Pattern Recognition and Graphics; Sinogram; Vision; Visual perception; Deep learning; Computed tomography; Computer-aided detection; Lung; Sinogram
• ISSN: 2524-4442; 2096-496X; 2524-4442
• 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.