TY - GEN
T1 - Task-based performance evaluation of deep neural network-based image denoising
AU - Li, Kaiyan
AU - Zhou, Weimin
AU - Li, Hua
AU - Anastasio, Mark A.
N1 - Publisher Copyright:
Copyright © 2021 SPIE.
PY - 2021
Y1 - 2021
N2 - Deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are commonly optimized and evaluated by use of traditional physical measures of image quality (IQ). However, the objective evaluation of IQ for such methods remains largely lacking. In this study, task-based IQ measures are used to evaluate the performance of DNN-based denoising methods. Specifically, we consider signal detection tasks under background-known-statistically conditions. The performance of the ideal observer (IO) and the Hotelling observer (HO) are quantified and detection efficiencies are computed to investigate the impact of the denoising operation on task performance. The experimental results show that, in the cases considered, the application of a denoising network generally results in a loss of task-relevant information. The impact of the depth of the denoising networks on task performance is also assessed. While mean squared error improved as the network depths were increased, signal detection performance degraded. These 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.
AB - Deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are commonly optimized and evaluated by use of traditional physical measures of image quality (IQ). However, the objective evaluation of IQ for such methods remains largely lacking. In this study, task-based IQ measures are used to evaluate the performance of DNN-based denoising methods. Specifically, we consider signal detection tasks under background-known-statistically conditions. The performance of the ideal observer (IO) and the Hotelling observer (HO) are quantified and detection efficiencies are computed to investigate the impact of the denoising operation on task performance. The experimental results show that, in the cases considered, the application of a denoising network generally results in a loss of task-relevant information. The impact of the depth of the denoising networks on task performance is also assessed. While mean squared error improved as the network depths were increased, signal detection performance degraded. These 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.
UR - http://www.scopus.com/inward/record.url?scp=85105486593&partnerID=8YFLogxK
U2 - 10.1117/12.2582324
DO - 10.1117/12.2582324
M3 - Conference contribution
AN - SCOPUS:85105486593
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Samuelson, Frank W.
A2 - Taylor-Phillips, Sian
PB - SPIE
T2 - Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment
Y2 - 15 February 2021 through 19 February 2021
ER -