Denoising Diffusion Models for Inpainting of Healthy Brain Tissue
Alicia Durrer Philippe C. Cattin Julia Wolleb
This paper is a contribution to the "BraTS 2023 Local Synthesis of Healthy
Brain Tissue via Inpainting Challenge". The task of this challenge is to
transform tumor tissue into healthy tissue in brain magnetic resonance (MR)
images. This idea originates from the problem that MR images can be evaluated
using automatic processing tools, however, many of these tools are optimized
for the analysis of healthy tissue. By solving the given inpainting task, we
enable the automatic analysis of images featuring lesions, and further
downstream tasks. Our approach builds on denoising diffusion probabilistic
models. We use a 2D model that is trained using slices in which healthy tissue
was cropped out and is learned to be inpainted again. This allows us to use the
ground truth healthy tissue during training. In the sampling stage, we replace
the slices containing diseased tissue in the original 3D volume with the slices
containing the healthy tissue inpainting. With our approach, we achieve
comparable results to the competing methods. On the validation set our model
achieves a mean SSIM of 0.7804, a PSNR of 20.3525 and a MSE of 0.0113. In
future we plan to extend our 2D model to a 3D model, allowing to inpaint the
region of interest as a whole without losing context information of neighboring
slices.