Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing

Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkuehler, Philippe C. Cattin

Keywords: denoising diffusion, diffusion models, segmentation, 3d images

TL;DR: We investigate how to apply diffusion models to 3d high resolution medical images.

Abstract:

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 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 task, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.