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| 001 | 1042642 | ||
| 005 | 20250526122859.0 | ||
| 024 | 7 | _ | |2 doi |a 10.48550/ARXIV.2505.11394 |
| 024 | 7 | _ | |2 datacite_doi |a 10.34734/FZJ-2025-02623 |
| 037 | _ | _ | |a FZJ-2025-02623 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Oberstrass, Alexander |b 0 |e Corresponding author |
| 245 | _ | _ | |a From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging |
| 260 | _ | _ | |b arXiv |c 2025 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1747889296_20947 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 520 | _ | _ | |a Comprehensive assessment of the various aspects of the brain's microstructure requires the use of complementary imaging techniques. This includes measuring the spatial distribution of cell bodies (cytoarchitecture) and nerve fibers (myeloarchitecture). The gold standard for cytoarchitectonic analysis is light microscopic imaging of cell-body stained tissue sections. To reveal the 3D orientations of nerve fibers, 3D Polarized Light Imaging (3D-PLI) has been introduced as a reliable technique providing a resolution in the micrometer range while allowing processing of series of complete brain sections. 3D-PLI acquisition is label-free and allows subsequent staining of sections after measurement. By post-staining for cell bodies, a direct link between fiber- and cytoarchitecture can potentially be established within the same section. However, inevitable distortions introduced during the staining process make a nonlinear and cross-modal registration necessary in order to study the detailed relationships between cells and fibers in the images. In addition, the complexity of processing histological sections for post-staining only allows for a limited number of samples. In this work, we take advantage of deep learning methods for image-to-image translation to generate a virtual staining of 3D-PLI that is spatially aligned at the cellular level. In a supervised setting, we build on a unique dataset of brain sections, to which Cresyl violet staining has been applied after 3D-PLI measurement. To ensure high correspondence between both modalities, we address the misalignment of training data using Fourier-based registration methods. In this way, registration can be efficiently calculated during training for local image patches of target and predicted staining. We demonstrate that the proposed method enables prediction of a Cresyl violet staining from 3D-PLI, matching individual cell instances. |
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| 650 | _ | 7 | |2 Other |a Image and Video Processing (eess.IV) |
| 650 | _ | 7 | |2 Other |a Computer Vision and Pattern Recognition (cs.CV) |
| 650 | _ | 7 | |2 Other |a FOS: Electrical engineering, electronic engineering, information engineering |
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| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Vaca, Esteban |b 1 |
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| 700 | 1 | _ | |0 P:(DE-Juel1)131642 |a Grässel, David |b 5 |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)170068 |a Schiffer, Christian |b 6 |u fzj |
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| 700 | 1 | _ | |0 P:(DE-Juel1)165746 |a Dickscheid, Timo |b 9 |u fzj |
| 773 | _ | _ | |a 10.48550/arXiv.2505.11394 |t arXiv |y 2025 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2505.11394 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1042642/files/Oberstrass_etal_2025_From_Fibre_Preprint.pdf |y OpenAccess |
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