001031454 001__ 1031454
001031454 005__ 20241107210038.0
001031454 037__ $$aFZJ-2024-05673
001031454 041__ $$aEnglish
001031454 1001_ $$0P:(DE-Juel1)171152$$aKropp, Jan-Oliver$$b0$$eCorresponding author$$ufzj
001031454 1112_ $$a8th BigBrain Workshop$$cPadua$$d2024-09-09 - 2024-09-11$$wItaly
001031454 245__ $$aStep by Step: Towards a gapless 1 micron BigBrain with Diffusion Models
001031454 260__ $$c2024
001031454 3367_ $$033$$2EndNote$$aConference Paper
001031454 3367_ $$2DataCite$$aOther
001031454 3367_ $$2BibTeX$$aINPROCEEDINGS
001031454 3367_ $$2DRIVER$$aconferenceObject
001031454 3367_ $$2ORCID$$aLECTURE_SPEECH
001031454 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1730963172_20213$$xAfter Call
001031454 502__ $$cHeinrich-Heine-University Düsseldorf
001031454 520__ $$aAdvances in microscopic imaging and high-performance computation have made it possible to analyze the complex cellular structure of the human brain in great detail. This progress has greatly aided in brain mapping and cell segmentation, leading to the development methods for automated analysis of tissue architecture and cell distribution in histological brain sections. However, histological image data can contain data gaps due to inevitable processing artifacts, which, despite careful precautions, may arise during histological lab work, such as missing sections, tissue tears, or inconsistent staining.To address this issue, we present a convolutional neural network model that reconstructs missing or corrupted data from surrounding tissue, while preserving precise cellular distributions. Our approach is based on recent advancements in image generation and involves utilizing a denoising diffusion probabilistic model (DDPM) that is trained on light-microscopy scans of cell-body stained histological sections. We extend this model with the RePaint method to impute missing or replace corrupted image data.To validate the model, we propose two new validation metrics based on two established deep learning models that were trained on the same type of data. In validation, we want to confirm a) the correct reproduction of cell statistics like cell size and count, and b) the generation of plausible cytoarchitectonic patterns, including brain area-specific laminar and columnar organization . We compare cell statistics using CPN, a cell segmentation model that provides precise cell statistics. Additionally, a model trained for cytoarchitecture classification is used to validate the structure of the inpainted regions by comparing how the inpainting process affects classification performance.We find that images generated by the proposed DDPM exhibit realistic and anatomically highly plausible cell distributions, effectively filling in data gaps resulting from histological artifacts. The model achieves low errors in cell statistics of less than 10% and high accuracies in cytoarchitecture classification of above 85%, even with inpainted regions as large as 50% of the input patch. Our results demonstrate the potential of the proposed generative model to improve the accuracy and completeness of analysis workflows for histological brain imaging data and to provide the basis for the development of future whole-brain human brain atlases.
001031454 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001031454 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x1
001031454 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x2
001031454 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x3
001031454 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b1$$ufzj
001031454 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b2$$ufzj
001031454 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
001031454 8564_ $$uhttps://events.hifis.net/event/1416/contributions/11426/
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001031454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171152$$aForschungszentrum Jülich$$b0$$kFZJ
001031454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b1$$kFZJ
001031454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b2$$kFZJ
001031454 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
001031454 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001031454 9141_ $$y2024
001031454 920__ $$lyes
001031454 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
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001031454 980__ $$aI:(DE-Juel1)INM-1-20090406
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