  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.
