MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

doi:doi:10.57702/5u6244j7

MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations.

BibTex:

@dataset{Kun_Han_and_Yifeng_Xiong_and_Chenyu_You_and_Pooya_Khosravi_and_Shanlin_sun_and_Xiangyi_Yan_and_James_Duncan_and_Xiaohui_Xie_2024,
    abstract = {Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations.},
    author = {Kun Han and Yifeng Xiong and Chenyu You and Pooya Khosravi and Shanlin sun and Xiangyi Yan and James Duncan and Xiaohui Xie},
    doi = {10.57702/5u6244j7},
    institution = {No Organization},
    keyword = {'3D Medical Images', 'Deep Generative Framework', 'Mask Generation'},
    month = {dec},
    publisher = {TIB},
    title = {MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation},
    url = {https://service.tib.eu/ldmservice/dataset/medgen3d--a-deep-generative-framework-for-paired-3d-image-and-mask-generation},
    year = {2024}
}