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Acquiring and annotating sufficient labeled data is crucial in developingaccurate and robust learning-based models, but obtaining such data can bechallenging in many medical image segmentation tasks. One promising solution isto synthesize realistic data with ground-truth mask annotations. However, noprior studies have explored generating complete 3D volumetric images withmasks. In this paper, we present MedGen3D, a deep generative framework that cangenerate paired 3D medical images and masks. First, we represent the 3D medicaldata as 2D sequences and propose the Multi-Condition Diffusion ProbabilisticModel (MC-DPM) to generate multi-label mask sequences adhering to anatomicalgeometry. Then, we use an image sequence generator and semantic diffusionrefiner conditioned on the generated mask sequences to produce realistic 3Dmedical images that align with the generated masks. Our proposed frameworkguarantees accurate alignment between synthetic images and segmentation maps.Experiments on 3D thoracic CT and brain MRI datasets show that our syntheticdata is both diverse and faithful to the original data, and demonstrate thebenefits for downstream segmentation tasks. We anticipate that MedGen3D'sability to synthesize paired 3D medical images and masks will prove valuable intraining deep learning models for medical imaging tasks.

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