Evaluating Automatic Warning Cues for Visual Search in Vascular Images

Visual search is a task that is performed in various application areas. Search can be aided by an automatic warning system, which highlights the sections that may contain targets and require the user's attention. The effect of imperfect automatic warnings on overall performance ultimately depends on the interplay between the user and the automatic warning system. While various studies exist, the different studies differ in several experimental variables including the nature of the visualisation itself. Studies in the medical area are relatively rare. We describe an experiment where users had to perform a visual search on a vascular structure, traversing a particular vessel linearly in search of possible errors made in an automatic segmentation. We find that only the case in which the warning system generates only false positives improves user time and error performance. We discuss this finding in relation to the findings of other studies