  The Ideal Observer (IO) performance has been advocated when optimizing
medical imaging systems for signal detection tasks. However, analytical
computation of the IO test statistic is generally intractable. To approximate
the IO test statistic, sampling-based methods that employ Markov-Chain Monte
Carlo (MCMC) techniques have been developed. However, current applications of
MCMC techniques have been limited to several object models such as a lumpy
object model and a binary texture model, and it remains unclear how MCMC
methods can be implemented with other more sophisticated object models. Deep
learning methods that employ generative adversarial networks (GANs) hold great
promise to learn stochastic object models (SOMs) from image data. In this
study, we described a method to approximate the IO by applying MCMC techniques
to SOMs learned by use of GANs. The proposed method can be employed with
arbitrary object models that can be learned by use of GANs, thereby the domain
of applicability of MCMC techniques for approximating the IO performance is
extended. In this study, both signal-known-exactly (SKE) and
signal-known-statistically (SKS) binary signal detection tasks are considered.
The IO performance computed by the proposed method is compared to that computed
by the conventional MCMC method. The advantages of the proposed method are
discussed.
