Pulmonary nodule detection in medical images: A survey. (May 2018)

• Record Type: Journal Article
• Authors: Zhang, Junjie; Xia, Yong; Cui, Hengfei; Zhang, Yanning
• Is Part Of: Biomedical signal processing and control. Volume 43(2018)
• Page Start: 138
• Page End: 147
• Publication Date: 2018-05
• Subjects: Lung cancer -- Pulmonary nodule detection -- Computer-aided detection -- Medical image analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.01.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Malignant nodules may be due to primary tumors or a metastasis and, given the importance of diagnosing early primary lung tumors, the detection of pulmonary nodules is critical. Therefore, a lot of research efforts have been devoted to the research on computer-aided detection (CADe) schemes for pulmonary nodule detection. This survey sheds light on what CADe schemes are really implementing to detect pulmonary nodules and which will in turn assist radiologist for better diagnosis. This paper provides a systematic depiction of both feature engineering- and deep learning-based CADe schemes, including the categories of pulmonary nodules, modalities of chest medical imaging, commonly used datasets with nodule annotations, and related publications in recent years. A comprehensive comparison and analyses of pulmonary nodule detection schemes proposed in the last three years are also presented.