Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks

TL;DR

This paper introduces a novel approach combining GANs and MCMC to approximate the Ideal Observer performance in medical imaging, extending applicability to complex object models beyond traditional methods.

Contribution

The study presents a method to use GAN-learned stochastic object models with MCMC for IO approximation, broadening the scope of MCMC applications in medical imaging.

Findings

The proposed GAN-MCMC method accurately approximates IO performance.

It extends MCMC applicability to complex object models learned by GANs.

Comparison shows advantages over traditional MCMC methods.

Abstract

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.