MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution

by   Jin Zhu, et al.

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 generalise 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 (1, 4]. 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.


page 3

page 7

page 18

page 19


Perceptual cGAN for MRI Super-resolution

Capturing high-resolution magnetic resonance (MR) images is a time consu...

Arbitrary Scale Super-Resolution for Brain MRI Images

Recent attempts at Super-Resolution for medical images used deep learnin...

Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing

Deep learning-based image processing is capable of creating highly appea...

Lesion Focused Super-Resolution

Super-resolution (SR) for image enhancement has great importance in medi...

High Resolution Medical Image Analysis with Spatial Partitioning

Medical images such as 3D computerized tomography (CT) scans and patholo...

Progressive Generative Adversarial Networks for Medical Image Super resolution

Anatomical landmark segmentation and pathology localization are importan...

Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples

Although high resolution isotropic 3D medical images are desired in clin...