Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks

Adam Wunderlich; Bart Goossens

doi:10.1117/12.2043105

11 March 2014 Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks

Adam Wunderlich, Bart Goossens

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90370F (2014) https://doi.org/10.1117/12.2043105
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

The majority of the literature on task-based image quality assessment has focused on lesion detection tasks, using the receiver operating characteristic (ROC) curve, or related variants, to measure performance. However, since many clinical image evaluation tasks involve both detection and estimation (e.g., estimation of kidney stone composition, estimation of tumor size), there is a growing interest in performance evaluation for joint detection and estimation tasks. To evaluate observer performance on such tasks, Clarkson introduced the estimation ROC (EROC) curve, and the area under the EROC curve as a summary figure of merit. In the present work, we propose nonparametric estimators for practical EROC analysis from experimental data, including estimators for the area under the EROC curve and its variance. The estimators are illustrated with a practical example comparing MRI images reconstructed from different k-space sampling trajectories.

Citation Download Citation

Adam Wunderlich and Bart Goossens "Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370F (11 March 2014); https://doi.org/10.1117/12.2043105

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