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Arbitrary Scale Super-Resolution for Medical Images
Author(s) -
Jin Zhu,
Chuan Tan,
Jingzhou Yang,
Guang Yang,
Píetro Lió
Publication year - 2021
Publication title -
international journal of neural systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.376
H-Index - 63
eISSN - 1793-6462
pISSN - 0129-0657
DOI - 10.1142/s0129065721500374
Subject(s) - computer science , artificial intelligence , magnification , image quality , computer vision , image resolution , scale (ratio) , medical imaging , image (mathematics) , segmentation , pattern recognition (psychology) , iterative reconstruction , image processing , physics , quantum mechanics
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.

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