Improving CAD performance by seamless insertion of pulmonary nodules in chest CT exams

Abstract

The availability of large medical image datasets is critical in training and testing of computer aided diagnosis (CAD) systems. However, collection of data and establishment of ground truth for medical images are both costly and difficult. To address this problem, we have developed an image composition tool that allows users to modify or supplement existing datasets by seamlessly inserting a clinical lesion extracted from a source image into a different location on a target image. In this study we focus on the application of this tool to the training of a CAD system designed to detect pulmonary nodules in chest CT. To compare the performance of a CAD system without and with the use of our image composition tool, we trained the system on two sets of data. The first training set was obtained from original CT cases, while the second set consisted of the first set plus nodules in the first set inserted into new locations. We then compared the performance of the two CAD systems in differentiating nodules from normal areas by testing each trained system against a fixed dataset containing natural nodules, and using the area under the ROC curve (AUC) as the figure of merit. The performance of the system trained with the augmented dataset was found to be significantly better than that trained with the original dataset under several training scenarios.