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

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          Abstract

          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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          Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

          Varun Gulshan, Lily Peng, Marc Coram … (2016)
          Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
          Article 0 comments Cited 1303 times – based on 0 reviews     Review now
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            Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

            Hoo-Chang Shin, Holger R. Roth, Mingchen Gao … (2016)
            Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
            Article 0 comments Cited 996 times – based on 0 reviews     Review now
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              Computed tomography--an increasing source of radiation exposure.

              David Brenner, Eric Hall (2008)
              Article 0 comments Cited 482 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xiaofeng Yang: xiaofeng.yang@emory.edu
                Journal
                Journal ID (nlm-ta): J Appl Clin Med Phys
                Journal ID (iso-abbrev): J Appl Clin Med Phys
                Journal ID (doi): 10.1002/(ISSN)1526-9914
                Journal ID (publisher-id): ACM2
                Title: Journal of Applied Clinical Medical Physics
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 1526-9914
                Publication date (Electronic): 11 December 2020
                Publication date Collection: January 2021
                Volume: 22
                Issue: 1 ( doiID: 10.1002/acm2.v22.1 )
                Pages: 11-36
                Affiliations
                [ 1 ] Department of Radiation Oncology Emory University Atlanta GA USA
                [ 2 ] Winship Cancer Institute Emory University Atlanta GA USA
                Author notes
                [*] [* ] Author to whom correspondence should be addressed. Xiaofeng Yang

                E‐mail: xiaofeng.yang@ 123456emory.edu .

                Article
                Publisher ID: ACM213121
                DOI: 10.1002/acm2.13121
                PMC ID: 7856512
                PubMed ID: 33305538
                SO-VID: fa4d707e-639a-4ba7-ad5b-37e00384dc2d
                Copyright © © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
                License:

                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.

                History
                Date received : 13 May 2020
                Date revision received : 12 November 2020
                Date accepted : 21 November 2020
                Page count
                Figures: 3, Tables: 10, Pages: 26, Words: 22381
                Funding
                Funded by: Emory Winship Cancer Institute
                Award ID: Pilot Grant
                Funded by: National Cancer Institute , open-funder-registry 10.13039/100000054;
                Funded by: National Institutes of Health , open-funder-registry 10.13039/100000002;
                Award ID: R01CA215718
                Categories
                Subject: Review Article
                Subject: Review Articles
                Custom metadata
                source-schema-version-number 2.0
                cover-date January 2021
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.7 mode:remove_FC converted:03.02.2021

                Keywords: ct,deep learning,image synthesis,mri,pet,radiation therapy
                Data availability:
                Keywords: ct, deep learning, image synthesis, mri, pet, radiation therapy

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