Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks

Weimin Zhou, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
PublisherSPIE
ISBN (Electronic)9781510633995
DOIs
StatePublished - Jan 1 2020
EventMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11316
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityHouston
Period2/19/202/20/20

Keywords

  • Generative adversarial networks
  • Ideal observer
  • Markov-Chain monte carlo
  • Signal detection theory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Zhou, W., & Anastasio, M. A. (2020). Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. In F. W. Samuelson, & S. Taylor-Phillips (Eds.), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment [113160D] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11316). SPIE. https://doi.org/10.1117/12.2549732