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Synthesis of CT images from digital body phantoms using CycleGAN [dataset]
- heidata.uni-heidelberg.de
zipUpdated Feb 23, 2023
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EmailClick to copy linkLink copiedCiteFrank Zöllner; Frank Zöllner (2023). Synthesis of CT images from digital body phantoms using CycleGAN [dataset] [Dataset]. http://doi.org/10.11588/DATA/7NRFYCzip(53512131857)Available download formatsUnique identifierhttps://doi.org/10.11588/DATA/7NRFYCDataset updatedFeb 23, 2023Dataset provided byheiDATAAuthorsFrank Zöllner; Frank ZöllnerLicenseAttribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automaticallyDataset funded byGerman Federal Ministry of Education and Research (BMBF)DescriptionThe potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms.
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Synthesis of CT images from digital body phantoms using CycleGAN [dataset] -...
- b2find.eudat.eu
Updated Aug 13, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCite(2025). Synthesis of CT images from digital body phantoms using CycleGAN [dataset] - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/196820de-c9de-5487-afb5-f6ffedbe4f9aDataset updatedAug 13, 2025LicenseMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automaticallyDescriptionThe potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms. Here, only the generated synthetic CT image data are provided. For generating body models as basis for synthetic CT generation you need to license the XCAT phantom (https://otc.duke.edu/technologies/xcat-library-of-anatomical-models-for-ct-imaging-research/).
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FacebookTwitterEmailSynthesis of CT images from digital body phantoms using CycleGAN [dataset]
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms.