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

Weimin Zhou, Umberto Villa, Mark A. Anastasio

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE transactions on medical imaging
StateAccepted/In press - 2023


  • Bayesian Ideal Observer
  • Biomedical imaging
  • generative adversarial networks
  • Generative adversarial networks
  • Generators
  • Markov-Chain Monte Carlo
  • Monte Carlo methods
  • Observers
  • Signal detection
  • Task analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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