Learning stochastic object models from imaging measurements

AmbientGAN

The objective optimization of image-derived statistics, including the test statistic of an observer for specific decision tasks, requires a characterization of all sources of variability in the measured data. To accomplish this, it is necessary to establish a stochastic object model (SOM) that describes the variability within a group of objects to-be imaged. In order for the SOM to be realistic, it is desirable to establish it by use of experimental image data, as opposed to establishing it in a non-data-driven manner. Deep learning methods that employ generative adversarial networks (GANs) hold promise for learning SOMs that can generate images that match distributions of training image data. However, because experimental data recorded by an imaging system represent noisy and indirect measurements of the object, conventional GANs cannot be directly employed for this task. Recently, an augmented GAN architecture named AmbientGAN was proposed that can characterize a distribution of images from noisy and indirect measurements of them and knowledge of the measurement operator. In this work, for the first time, we investigate AmbientGANs for establishing SOMs by use of noisy imaging measurements. The learned SOM is evaluated by comparing receiver operating characteristics (ROC) curves in binary signal detection tasks with respect to the true object model.

(a)-(e) True Radon transform data. (f)-(j) Fake Radon transform data generated by acting the Radon transform on the images produced by AmbientGAN.

(a)-(e) Object images produced by the true SOM. (f)-(j) Fake object images generated by the AmbientGAN.

(a) Hotelling template computed by use of true images for the detection task corresponding to the signal with width 1; (b) Hotelling template computed by use of fake images for the detection task corresponding to the signal with width 1; (c) Center line profiles of (a) and (b); (d) Hotelling template computed by use of true images for the detection task corresponding to the signal with width 3; (e) Hotelling template computed by use of fake images for the detection task corresponding to the signal with width 3; (f) Center line profiles of (d) and (e); (g) ROC curves corresponding to HOs computed by the true and fake images.

Publications

  1. Zhou, W., Bhadra, S., Brooks, F. J., Granstedt, J. L., Li, H., & Anastasio, M. A. (2021, February). Advancing the AmbientGAN for learning stochastic object models. In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment (Vol. 11599, p. 115990A). International Society for Optics and Photonics.
  2. Zhou, W., Bhadra, S., Brooks, F. J., Li, H., & Anastasio, M. A. (2020, March). Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. In Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment (Vol. 11316, p. 113160Q). International Society for Optics and Photonics.