1 research outputs found
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
It is widely accepted that optimization of medical imaging system performance
should be guided by task-based measures of image quality (IQ). Task-based
measures of IQ quantify the ability of an observer to perform a specific task
such as detection or estimation of a signal (e.g., a tumor). For binary signal
detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of
observer performance and has been advocated for use in optimizing medical
imaging systems and data-acquisition designs. Except in special cases,
determination of the IO test statistic is analytically intractable.
Markov-chain Monte Carlo (MCMC) techniques can be employed to approximate IO
detection performance, but their reported applications have been limited to
relatively simple object models. In cases where the IO test statistic is
difficult to compute, the Hotelling Observer (HO) can be employed. To compute
the HO test statistic, potentially large covariance matrices must be accurately
estimated and subsequently inverted, which can present computational
challenges. This work investigates supervised learning-based methodologies for
approximating the IO and HO test statistics. Convolutional neural networks
(CNNs) and single-layer neural networks (SLNNs) are employed to approximate the
IO and HO test statistics, respectively. Numerical simulations were conducted
for both signal-known-exactly (SKE) and signal-known-statistically (SKS) signal
detection tasks. The performances of the supervised learning methods are
assessed via receiver operating characteristic (ROC) analysis and the results
are compared to those produced by use of traditional numerical methods or
analytical calculations when feasible. The potential advantages of the proposed
supervised learning approaches for approximating the IO and HO test statistics
are discussed.Comment: IEEE Transactions on Medical Imaging (Early Access), 201