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.

Data and Resources

Cite this as

Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, Shanlin sun, Xiangyi Yan, James Duncan, Xiaohui Xie (2024). Dataset: MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation. https://doi.org/10.57702/5u6244j7

DOI retrieved: December 3, 2024

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2304.04106
Author Kun Han
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Yifeng Xiong
Chenyu You
Pooya Khosravi
Shanlin sun
Xiangyi Yan
James Duncan
Xiaohui Xie