TY - JOUR
T1 - Impact of deep learning-based image super-resolution on binary signal detection
AU - Zhang, Xiaohui
AU - Kelkar, Varun A.
AU - Granstedt, Jason
AU - Li, Hua
AU - Anastasio, Mark A.
N1 - Funding Information:
This work was supported in part by the National Institutes for Health, Award Nos. R01EB020604, R01EB023045, R01NS102213, R01CA233873, and R21CA223799. The authors greatly appreciate Michael X. Wu for proofreading the manuscript carefully and thoughtfully. Preliminary results of this work were presented at SPIE Medical Imaging 2021 and published as an SPIE Proceedings paper.51
Publisher Copyright:
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed using traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of IQ that are relevant to medical imaging tasks remains largely unexplored. We investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network and the super-resolution generative adversarial network, were trained using simulated medical image data. Binary signal-known-exactly with background-known-statistically and signal-known-statistically with background-known-statistically detection tasks were formulated. Numerical observers (NOs), which included a neural network-approximated ideal observer and common linear NOs, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task performance was quantified. In addition, the utility of DL-SR for improving the task performance of suboptimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR improved traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and even degraded it. It was observed that DL-SR improved the task performance of suboptimal observers under certain conditions. Conclusions: Our study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.
AB - Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed using traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of IQ that are relevant to medical imaging tasks remains largely unexplored. We investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network and the super-resolution generative adversarial network, were trained using simulated medical image data. Binary signal-known-exactly with background-known-statistically and signal-known-statistically with background-known-statistically detection tasks were formulated. Numerical observers (NOs), which included a neural network-approximated ideal observer and common linear NOs, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task performance was quantified. In addition, the utility of DL-SR for improving the task performance of suboptimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR improved traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and even degraded it. It was observed that DL-SR improved the task performance of suboptimal observers under certain conditions. Conclusions: Our study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.
KW - deep learning-based image super-resolution
KW - numerical observers
KW - objective image quality assessment
KW - Rayleigh detection task
UR - http://www.scopus.com/inward/record.url?scp=85122618006&partnerID=8YFLogxK
U2 - 10.1117/1.JMI.8.6.065501
DO - 10.1117/1.JMI.8.6.065501
M3 - Article
C2 - 34796251
AN - SCOPUS:85122618006
VL - 8
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
SN - 2329-4302
IS - 6
M1 - 065501
ER -