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| Home > Publications database > Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain > print |
| 001 | 1024072 | ||
| 005 | 20250203103104.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2311.16821 |2 doi |
| 024 | 7 | _ | |a 10.48550/arXiv.2311.16821 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01952 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01952 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kropp, Jan-Oliver |0 P:(DE-Juel1)171152 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain |
| 260 | _ | _ | |c 2023 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1714546024_30712 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Recent 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. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015) |0 G:(DE-HGF)InterLabs-0015 |c InterLabs-0015 |x 1 |
| 536 | _ | _ | |a Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62) |0 G:(DE-Juel-1)E.40401.62 |c E.40401.62 |x 2 |
| 536 | _ | _ | |a DFG project 313856816 - SPP 2041: Computational Connectomics (313856816) |0 G:(GEPRIS)313856816 |c 313856816 |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Image and Video Processing (eess.IV) |2 Other |
| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a FOS: Electrical engineering, electronic engineering, information engineering |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 1 |u fzj |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 2 |u fzj |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |u fzj |
| 773 | _ | _ | |a 10.48550/arXiv.2311.16821 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1024072/files/Kropp_et_al_2023_Denoising.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/1024072/files/Kropp_et_al_2023_Denoising.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/1024072/files/Kropp_et_al_2023_Denoising.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/1024072/files/Kropp_et_al_2023_Denoising.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/1024072/files/Kropp_et_al_2023_Denoising.jpg?subformat=icon-640 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171152 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 914 | 1 | _ | |y 2024 |
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| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | 1 | _ | |a FullTexts |
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