Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks

Abstract

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 (ALROC). 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 developed and implemented for approximating the Ideal Observer that maximizes the area under the LROC curve for signal detection-localization tasks. A background-known-exactly (BKE) case was considered. The resulting LROC curve and ALROC value are compared to those produced by an analytical calculation.