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

Weimin Zhou; Mark A. Anastasio

doi:10.1117/12.2513016

4 March 2019 Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks

Weimin Zhou, Mark A. Anastasio

Author Affiliations +

Proceedings Volume 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment; 1095209 (2019) https://doi.org/10.1117/12.2513016
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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 (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 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 A_LROC value are compared to those produced by an analytical calculation.

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Weimin Zhou and Mark A. Anastasio "Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks", Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 1095209 (4 March 2019); https://doi.org/10.1117/12.2513016

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