Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.

翻译：去噪扩散模型近期在许多图像生成任务中取得了最先进的性能。然而，它们需要大量的计算资源。这限制了其在医学任务中的应用，因为医学领域常需处理大型三维体数据，例如高分辨率三维数据。本文提出了多种降低三维扩散模型资源消耗的方法，并将其应用于三维图像数据集。本论文的主要贡献是内存高效的基于图像块的扩散模型 \textit{PatchDDM}，该模型在推理阶段可应用于整个体数据，而训练仅需在图像块上进行。虽然所提出的扩散模型可应用于任何图像生成任务，但我们在 BraTS2020 数据集的肿瘤分割任务上评估了该方法，并证明其能够生成有意义的三维分割结果。