Denoising Diffusion Models for Inpainting of Healthy Brain Tissue

• URL: http://arxiv.org/abs/2402.17307v2
• Date: Wed, 23 Oct 2024 14:42:07 GMT
• Title: Denoising Diffusion Models for Inpainting of Healthy Brain Tissue
• Authors: Alicia Durrer, Philippe C. Cattin, Julia Wolleb,
• Abstract summary: 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. We use a 2D model that is trained using slices in which healthy tissue was cropped out and is learned to be inpainted again.
• Score: 0.7022492404644499
• License:
• Abstract: 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.

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