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Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images

Please always quote using this URN: urn:nbn:de:bvb:20-opus-172185
  • Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients withEven as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.show moreshow less

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Metadaten
Author: Koshino Kazuhino, Rudolf A. Werner, Fuijo Toriumi, Mehrbod S. Javadi, Martin G. Pomper, Lilja B. Solnes, Franco Verde, Takahiro Higuchi, Steven P. Rowe
URN:urn:nbn:de:bvb:20-opus-172185
Document Type:Journal article
Faculties:Medizinische Fakultät / Klinik und Poliklinik für Nuklearmedizin
Language:English
Parent Title (English):Tomography
Year of Completion:2018
Volume:4
Issue:4
Pagenumber:159–163
Source:Tomography (2018) 4(4): 159–163. doi: 10.18383/j.tom.2018.00042
DOI:https://doi.org/10.18383/j.tom.2018.00042
Sonstige beteiligte Institutionen:Johns Hopkins School of Medicine
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
GND Keyword:Magnetresonanztomografie
Tag:AI; DCGAN; GAN; MRI; artificial intelligence; machine learning; magnetic resonance imaging; stroke
Release Date:2019/01/30
EU-Project number / Contract (GA) number:701983
OpenAIRE:OpenAIRE
Licence (German):License LogoCC BY-NC-ND: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell, Keine Bearbeitungen 4.0 International