A review on medical imaging synthesis using deep learning and its clinical applications

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study desi...

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Detalles Bibliográficos
Autores principales: Wang, Tonghe, Lei, Yang, Fu, Yabo, Wynne, Jacob F., Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Review Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856512/
https://www.ncbi.nlm.nih.gov/pubmed/33305538
http://dx.doi.org/10.1002/acm2.13121
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author Wang, Tonghe
Lei, Yang
Fu, Yabo
Wynne, Jacob F.
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
author_facet Wang, Tonghe
Lei, Yang
Fu, Yabo
Wynne, Jacob F.
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
author_sort Wang, Tonghe
collection PubMed
description This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.
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spelling pubmed-78565122021-02-05 A review on medical imaging synthesis using deep learning and its clinical applications Wang, Tonghe Lei, Yang Fu, Yabo Wynne, Jacob F. Curran, Walter J. Liu, Tian Yang, Xiaofeng J Appl Clin Med Phys Review Articles This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion. John Wiley and Sons Inc. 2020-12-11 /pmc/articles/PMC7856512/ /pubmed/33305538 http://dx.doi.org/10.1002/acm2.13121 Text en © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Articles
Wang, Tonghe
Lei, Yang
Fu, Yabo
Wynne, Jacob F.
Curran, Walter J.
Liu, Tian
Yang, Xiaofeng
A review on medical imaging synthesis using deep learning and its clinical applications
title A review on medical imaging synthesis using deep learning and its clinical applications
title_full A review on medical imaging synthesis using deep learning and its clinical applications
title_fullStr A review on medical imaging synthesis using deep learning and its clinical applications
title_full_unstemmed A review on medical imaging synthesis using deep learning and its clinical applications
title_short A review on medical imaging synthesis using deep learning and its clinical applications
title_sort review on medical imaging synthesis using deep learning and its clinical applications
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856512/
https://www.ncbi.nlm.nih.gov/pubmed/33305538
http://dx.doi.org/10.1002/acm2.13121
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