用扩散模型和隐式神经表示生成更真实多样的肝脏3D数据集
Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representations
- 结合扩散模型与隐式神经表示生成新肝脏形状
- 显著提升数据集多样性，缓解数据稀缺问题
- 适合医学图像重建与生成任务的研究者
尽管公开的3D医学形态数据集数量不断增长，但许多数据集存在组织混乱和伪影问题，限制了鲁棒模型的开发，尤其影响精准3D重建。本文分析现有肝脏3D形状数据集现状，提出一种基于扩散模型与隐式神经表示（INRs）的数据增强方法。该方法利用扩散模型的生成能力，合成多样且真实的3D肝脏形状，涵盖广泛的解剖变异，有效应对数据不足挑战。实验表明，该方法显著提升了数据集多样性，为3D肝脏重建与生成提供可扩展的解决方案。此外，研究建议该方法可推广至其他3D医学影像下游任务。
原文摘要 · Abstract (English)
While the availability of open 3D medical shape datasets is increasing, offering substantial benefits to the research community, we have found that many of these datasets are, unfortunately, disorganized and contain artifacts. These issues limit the development and training of robust models, particularly for accurate 3D reconstruction tasks. In this paper, we examine the current state of available 3D liver shape datasets and propose a solution using diffusion models combined with implicit neural representations (INRs) to augment and expand existing datasets. Our approach utilizes the generative capabilities of diffusion models to create realistic, diverse 3D liver shapes, capturing a wide range of anatomical variations and addressing the problem of data scarcity. Experimental results indicate that our method enhances dataset diversity, providing a scalable solution to improve the accuracy and reliability of 3D liver reconstruction and generation in medical applications. Finally, we suggest that diffusion models can also be applied to other downstream tasks in 3D medical imaging.
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