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Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
Signal Processing · Submitted 29 May 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.
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Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. Computations of likelihood ratios are analytically intractable in the majority of cases. Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. However, the applications of MCMC methods have been limited to relatively simple object models. Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. | background 1.00 | 0.04 | 0.03 | asserted 0.94 |
| s2 | When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. | background 1.00 | 0.08 | 0.04 | asserted 1.00 |
| s3 | Computations of likelihood ratios are analytically intractable in the majority of cases. | background 0.92 | 0.06 | 0.02 | asserted 1.00 |
| s4 | Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. | background 0.91 | 0.05 | 0.02 | asserted 1.00 |
| s5 | However, the applications of MCMC methods have been limited to relatively simple object models. | gap 0.61 | 0.04 | 0.02 | asserted 1.00 |
| s6 | Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. | background 0.91 | 0.05 | 0.04 | asserted 1.00 |
| s7 | In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. | contribution 1.00 | 0.76 | 0.05 | unclear 0.55 |
| s8 | Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. | method 0.78 | 0.81 | 0.03 | asserted 0.96 |
| s9 | The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. | method 0.95 | 0.68 | 0.02 | asserted 0.96 |
| s10 | The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. | method 0.89 | 0.90 | 0.09 | asserted 0.92 |
| s11 | The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored. | interpretation 0.71 | 0.67 | 0.16 | hedged possibility 0.79 |
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The paper
Original abstract
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. Computations of likelihood ratios are analytically intractable in the majority of cases. Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. However, the applications of MCMC methods have been limited to relatively simple object models. Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.
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To achieve this, one may establish a stochastic object model (SOM) from experimental data by training an AmbientGAN [ref] .
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs · Summary · 0.88
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At each iteration of the training, the CNN weight parameters were updated by minimizing the empirical error function on mini-batches by use of the Adam algorithm [ref] , which is a stochastic gradient-based method.
Adam: A Method for Stochastic Optimization · CNN training details · 1.00
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A LeakyReLU activation function [ref] was applied to the feature maps produced by each CONV layer and a softmax function was applied to the output of the fully connected layer.
Striving for Simplicity: The All Convolutional Net · CNN training details · 0.95
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