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：去噪扩散模型近日已在诸多图像生成任务中取得最先进性能，但其计算资源需求巨大。这限制了它们在医学任务中的应用——我们常需处理高分辨率三维数据这类大体积3D图像。本研究提出了多种降低3D扩散模型资源消耗的方法，并将其应用于3D图像数据集。本文核心贡献在于提出存算高效的图像块扩散模型\textit{PatchDDM}：该模型仅需在图像块上进行训练，即可在推理阶段应用于全体积数据。虽然所提扩散模型可适用于任意图像生成任务，我们特在BraTS2020数据集的肿瘤分割任务中验证该方法，结果表明其能生成有意义的二维分割结果。