Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks

• URL: http://arxiv.org/abs/2304.00433v1
• Date: Sun, 2 Apr 2023 02:51:50 GMT
• Title: Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
• Authors: Weimin Zhou, Umberto Villa, Mark A. Anastasio
• Abstract summary: The Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM) for evaluating and optimizing medical imaging systems. A sampling-based method that employs Markov-Chain Monte Carlo (MCMC) techniques was previously proposed to estimate the IO performance. In this study, a novel MCMC method that employs a generative adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and evaluated.
• Score: 12.521662223741671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task. The performance of the Bayesian Ideal Observer (IO) sets an upper limit among all observers, numerical or human, and has been advocated for use as a figure-of-merit (FOM) for evaluating and optimizing medical imaging systems. However, the IO test statistic corresponds to the likelihood ratio that is intractable to compute in the majority of cases. A sampling-based method that employs Markov-Chain Monte Carlo (MCMC) techniques was previously proposed to estimate the IO performance. However, current applications of MCMC methods for IO approximation have been limited to a small number of situations where the considered distribution of to-be-imaged objects can be described by a relatively simple stochastic object model (SOM). As such, there remains an important need to extend the domain of applicability of MCMC methods to address a large variety of scenarios where IO-based assessments are needed but the associated SOMs have not been available. In this study, a novel MCMC method that employs a generative adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and evaluated. The MCMC-GAN method was quantitatively validated by use of test-cases for which reference solutions were available. The results demonstrate that the MCMC-GAN method can extend the domain of applicability of MCMC methods for conducting IO analyses of medical imaging systems.

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