Receiver Operating Characteristic (ROC) Analysis of Image Search-and-Localize Tasks

Receiver operating characteristic (ROC) analysis for the common image search-and-localize task, in which readers search an image for lesion or lesions not knowing a priori any exists, has been studied for over four decades. However, a satisfactory solution seems elusive.

We show that the ROC curve predictive of clinical outcomes where readers are penalized appropriately for not correctly localizing known lesions cannot be obtained because it is a missing data problem. Further, this ROC curve is between the case-based ROC curve where readers are not penalized and the lesion-based ROC curve where penalty applies. Moreover, the lesion-based ROC curve is the LROC curve proposed by Starr et al. We show maximum-likelihood (ML) estimation of the LROC curve, validation of this procedure with Monte Carlo simulations, and its application to reader ROC datasets.

Monte Carlo simulations validated ML estimation of area under the LROC curve (AUC) and its variance. Example applications showed that ML estimate of LROC curve fits experimental datasets.

The ROC curve predictive of clinical performance cannot be estimated from reader ROC data alone because it is a missing data problem, and is between the case-based ROC curve where readers are not penalized for not correctly identifying known lesions and the lesion-based ROC curve where penalty applies. The lesion-based ROC curve is the LROC curve proposed by Starr et al. and can be estimated via ML estimation.