Assessing the impact of deep neural network-based image denoising on binary signal detection tasks

[Collaborative project with Professor Anastasio.]

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.



Related publications

1. Kaiyan Li, Weimin Zhou, Hua Li and M. A. Anastasio, “Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks,” in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2021.3076810.
2. Kaiyan Li, Weimin Zhou, Hua Li, and Mark A. Anastasio “Task-based performance evaluation of deep neural network-based image denoising“, Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 115990L