View article

In medical imaging, task-based measures of image quality (IQ) have been commonly employed to assess and optimize imaging systems. To evaluate task-based measures of IQ, the performance of an observer on a relevant task is quantified. For a binary signal detection task, the Bayesian Ideal Observer sets an upper performance limit in a sense that it maximizes the area under the receiver operating characteristic (ROC) curve (AUC). When a joint signal detection and localization (detection-localization) task is considered, the modified generalized likelihood ratio test (MGLRT) has been advocated as an optimal decision strategy to maximize the area under the localization ROC (LROC) curve (A_LROC). However, analytical computation of likelihood ratios employed in the MGLRT is generally intractable. In this work, a supervised learning-based method that employs convolutional neural networks (CNNs) is …