Arbitrary scale super-resolution for medical images.
File(s)
Published version
Author(s)
Zhu, Jin
Tan, Chuan
Yang, Junwei
Yang, Guang
Lio', Pietro
Type
Journal Article
Abstract
Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalize over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with generative adversarial networks (GANs) to super-resolve medical images at any scale of magnification in [Formula: see text]. Compared to state-of-the-art SISR algorithms on single-modal magnetic resonance (MR) brain images (OASIS-brains) and multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity performance and the best perceptual quality with the smallest model size. We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT). The source code of our work is also public. Thus, MIASSR has the potential to become a new foundational pre-/post-processing step in clinical image analysis tasks such as reconstruction, image quality enhancement, and segmentation.
Date Issued
2021-07-24
Date Acceptance
2021-07-12
Citation
International Journal of Neural Systems, 2021, 31 (10), pp.1-20
ISSN
0129-0657
Publisher
World Scientific Publishing
Start Page
1
End Page
20
Journal / Book Title
International Journal of Neural Systems
Volume
31
Issue
10
Copyright Statement
© The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of theCreative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium,provided the original work is properly cited.
License URL
Sponsor
British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/34304719
Grant Number
PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
Subjects
Super-resolution
generative adversarial networks
image processing
medical image analysis
meta learning
transfer learning
Publication Status
Published
Coverage Spatial
Singapore
Date Publish Online
2021-07-24