Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
Alicia Durrer Julia Wolleb Florentin Bieder Paul Friedrich Lester Melie-Garcia Mario Ocampo-Pineda Cosmin I. Bercea Ibrahim E. Hamamci Benedikt Wiestler Marie Piraud Özgür Yaldizli Cristina Granziera Bjoern H. Menze Philippe C. Cattin Florian Kofler
Monitoring diseases that affect the brain's structural integrity requires
automated analysis of magnetic resonance (MR) images, e.g., for the evaluation
of volumetric changes. However, many of the evaluation tools are optimized for
analyzing healthy tissue. To enable the evaluation of scans containing
pathological tissue, it is therefore required to restore healthy tissue in the
pathological areas. In this work, we explore and extend denoising diffusion
models for consistent inpainting of healthy 3D brain tissue. We modify
state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as
well as 3D latent and 3D wavelet diffusion models, and train them to synthesize
healthy brain tissue. Our evaluation shows that the pseudo-3D model performs
best regarding the structural-similarity index, peak signal-to-noise ratio, and
mean squared error. To emphasize the clinical relevance, we fine-tune this
model on data containing synthetic MS lesions and evaluate it on a downstream
brain tissue segmentation task, whereby it outperforms the established FMRIB
Software Library (FSL) lesion-filling method.