Computerized lung nodule detection: Effect of image annotation schemes for conveying results to radiologists

We have developed a computerized method to automatically identify lung nodules in thoracic computed tomography (CT) scans. Since the ultimate goal of such a method is to improve human detection performance, the process through which computer results are conveyed to the radiologist must be considered. Detection results are presented through an interface that automatically places a circle around the detected structure in only one section in which that structure may appear. Consequently, an inappropriate choice of section could result in an actual nodule detected by the computer but not properly indicated to the radiologist, thus reducing the potential positive impact of that detection on the radiologist's decision-making process. The automated detection method was applied to 38 diagnostic CT scans with an overall sensitivity of 71% and 0.5 false-positive detections per section; however, when these results were converted automatically to annotations on the output images for human visualization, 8.6% of the computer-detected nodules received annotations that failed to encompass a portion of the actual nodule. Thus, the "effective sensitivity" of the automated detection method (i.e., a performance paradigm that considers the eventual human interaction with system output) was reduced.