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001024072 0247_ $$2doi$$a10.48550/ARXIV.2311.16821
001024072 0247_ $$2doi$$a10.48550/arXiv.2311.16821
001024072 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01952
001024072 037__ $$aFZJ-2024-01952
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001024072 1001_ $$0P:(DE-Juel1)171152$$aKropp, Jan-Oliver$$b0$$eCorresponding author$$ufzj
001024072 245__ $$aDenoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain
001024072 260__ $$barXiv$$c2023
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001024072 520__ $$aRecent advances in imaging and high-performance computing have made it possible to image the entire human brain at the cellular level. This is the basis to study the multi-scale architecture of the brain regarding its subdivision into brain areas and nuclei, cortical layers, columns, and cell clusters down to single cell morphology Methods for brain mapping and cell segmentation exploit such images to enable rapid and automated analysis of cytoarchitecture and cell distribution in complete series of histological sections. However, the presence of inevitable processing artifacts in the image data caused by missing sections, tears in the tissue, or staining variations remains the primary reason for gaps in the resulting image data. To this end we aim to provide a model that can fill in missing information in a reliable way, following the true cell distribution at different scales. Inspired by the recent success in image generation, we propose a denoising diffusion probabilistic model (DDPM), trained on light-microscopic scans of cell-body stained sections. We extend this model with the RePaint method to impute missing or replace corrupted image data. We show that our trained DDPM is able to generate highly realistic image information for this purpose, generating plausible cell statistics and cytoarchitectonic patterns. We validate its outputs using two established downstream task models trained on the same data.
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001024072 536__ $$0G:(GEPRIS)313856816$$aDFG project 313856816 - SPP 2041: Computational Connectomics (313856816)$$c313856816$$x3
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001024072 650_7 $$2Other$$aImage and Video Processing (eess.IV)
001024072 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001024072 650_7 $$2Other$$aFOS: Electrical engineering, electronic engineering, information engineering
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001024072 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b1$$ufzj
001024072 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
001024072 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
001024072 773__ $$a10.48550/arXiv.2311.16821
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