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. However, no prior studies have explored generating complete 3D volumetric images with masks. In this paper, we present MedGen3D, a deep generative framework that can generate paired 3D medical images and masks. First, we represent the 3D medical data as 2D sequences and propose the Multi-Condition Diffusion Probabilistic Model (MC-DPM) to generate multi-label mask sequences adhering to anatomical geometry. Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks. Our proposed framework guarantees accurate alignment between synthetic images and segmentation maps. Experiments on 3D thoracic CT and brain MRI datasets show that our synthetic data is both diverse and faithful to the original data, and demonstrate the benefits for downstream segmentation tasks. We anticipate that MedGen3D's ability to synthesize paired 3D medical images and masks will prove valuable in training deep learning models for medical imaging tasks.

翻译：获取并标注充足的标签数据对开发准确且鲁棒的基于学习的模型至关重要，但在许多医学图像分割任务中，获取此类数据颇具挑战。一个颇有前景的解决方案是合成带有真实掩膜标注的现实数据。然而，此前尚无研究探索生成带有掩膜的完整三维体积图像。本文提出MedGen3D，一种能够生成成对三维医学图像与掩膜的深度生成框架。首先，我们将3D医学数据表示为2D序列，并提出多条件扩散概率模型（MC-DPM）以生成符合解剖几何结构的多标签掩膜序列。随后，我们利用基于生成掩膜序列的图像序列生成器与语义扩散精炼器，生成与对应掩膜对齐的真实3D医学图像。该框架确保了合成图像与分割图之间的精确对齐。在3D胸部CT与脑部MRI数据集上的实验表明，我们合成的数据兼具多样性与原始数据保真度，并验证了其在下游分割任务中的优势。我们预期MedGen3D合成成对3D医学图像与掩膜的能力，将在训练用于医学成像任务的深度学习模型中发挥重要价值。