Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks

TL;DR

This study evaluates how deep neural network-based image denoising affects binary signal detection in medical imaging, emphasizing the importance of task-specific quality measures over traditional image quality metrics.

Contribution

It introduces task-based image quality measures to objectively assess the impact of DNN denoising on detection tasks in medical images, revealing potential information loss.

Findings

Denoising can reduce task-relevant information in images.

Network depth influences detection performance.

Objective IQ evaluation is crucial for medical imaging applications.

Abstract

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed to optimize and evaluate these methods. However, the objective evaluation of IQ for the DNN-based denoising methods remains largely lacking. In this work, we evaluate the performance of DNN-based denoising methods by use of task-based IQ measures. Specifically, binary signal detection tasks under signal-known-exactly (SKE) with background-known-statistically (BKS) conditions are considered. The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance. The numerical results indicate that, in the cases considered, the application of a denoising network can result in a loss of task-relevant information in the image. The impact of the depth of the denoising networks on task performance is also assessed. The presented results highlight the need for the objective evaluation of IQ for DNN-based denoising technologies and may suggest future avenues for improving their effectiveness in medical imaging applications.