Synthesis of CT images from digital body phantoms using CycleGAN [dataset]

DOI

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.

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/industry-investors/available-technologies/xcat/).

Identifier
DOI https://doi.org/10.11588/data/7NRFYC
Related Identifier https://doi.org/10.1007/s11548-019-02042-9
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/7NRFYC
Provenance
Creator Zöllner, Frank (Universität Heidelberg)
Publisher heiDATA
Contributor Zöllner, Frank
Publication Year 2022
Funding Reference German Federal Ministry of Education and Research (BMBF), 13GW0388A
Rights CC BY-NC 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by-nc/4.0
OpenAccess true
Contact Zöllner, Frank (Universität Heidelberg)
Representation
Resource Type image data is provided in nrrd file format (http://teem.sourceforge.net/nrrd/index.html); Dataset
Format application/zip
Size 53512131857
Version 1.0
Discipline Life Sciences; Medicine