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Arbitrary Scale Super-Resolution for Brain MRI Images
Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves hi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256400/ http://dx.doi.org/10.1007/978-3-030-49161-1_15 |
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author | Tan, Chuan Zhu, Jin Lio’, Pietro |
author_facet | Tan, Chuan Zhu, Jin Lio’, Pietro |
author_sort | Tan, Chuan |
collection | PubMed |
description | Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20[Formula: see text] on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images. |
format | Online Article Text |
id | pubmed-7256400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72564002020-05-29 Arbitrary Scale Super-Resolution for Brain MRI Images Tan, Chuan Zhu, Jin Lio’, Pietro Artificial Intelligence Applications and Innovations Article Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20[Formula: see text] on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images. 2020-05-06 /pmc/articles/PMC7256400/ http://dx.doi.org/10.1007/978-3-030-49161-1_15 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Tan, Chuan Zhu, Jin Lio’, Pietro Arbitrary Scale Super-Resolution for Brain MRI Images |
title | Arbitrary Scale Super-Resolution for Brain MRI Images |
title_full | Arbitrary Scale Super-Resolution for Brain MRI Images |
title_fullStr | Arbitrary Scale Super-Resolution for Brain MRI Images |
title_full_unstemmed | Arbitrary Scale Super-Resolution for Brain MRI Images |
title_short | Arbitrary Scale Super-Resolution for Brain MRI Images |
title_sort | arbitrary scale super-resolution for brain mri images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256400/ http://dx.doi.org/10.1007/978-3-030-49161-1_15 |
work_keys_str_mv | AT tanchuan arbitraryscalesuperresolutionforbrainmriimages AT zhujin arbitraryscalesuperresolutionforbrainmriimages AT liopietro arbitraryscalesuperresolutionforbrainmriimages |