Correlation between Biopsy Confirmed Cases and Radiologist’s Annotations in the Detection of Lung Nodules by Expanding the Diagnostic Database Using Content Based Image Retrieval

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate and available ground truth is critical and time consuming. In this study, we have explored Lung Image Database Consortium (LIDC) database containing pulmonary computed tomography (CT) scans, and we have implemented content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying CBIR method iteratively and using pathologically confirmed cases, we expand the set of diagnosed data available for CAD systems from 17 nodules to 121 nodules. We evaluated the method by implementing a CAD system that uses various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems. It also indicated that little knowledge of biopsy confirmed cases not only assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies too.