Traditional detection system performance metrics, such a probability of detection and probability of false alarm, depend only on how the system responds to individual target-sized regions-of-interest (ROIs). The composition of the larger scene does not affect those metrics. There are circumstances however, where a user of a detection system wants to know, "For a given cue, what is the probability that the cue is correct?" or perhaps the detector is being used to determine a property of the overall scene. As an example of the latter case, suppose the detection system is looking for diseased cells in a tissue sample. Even if only one diseased cell exists, the whole "scene" represents a diseased individual. In both cases, the user-perspective or the scene-based perspective, the natural performance metrics depend on the scene content, especially the numbers of target and confuser ROIs. This paper defines scene-content dependent (SCD) performance metrics for detection systems, develops a theory for computing them, and illustrates properties of the metrics with examples. The SCD performance theory enabled determination of the example metrics in about two hours of computation; whereas Monte Carlo methods would have taken almost a year and direct testing would have been almost impossible.