001018410 001__ 1018410
001018410 005__ 20231123201916.0
001018410 037__ $$aFZJ-2023-04791
001018410 041__ $$aEnglish
001018410 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$eCorresponding author$$ufzj
001018410 1112_ $$a7th BigBrain Workshop$$cReykjavík$$d2023-10-04 - 2023-10-06$$wIceland
001018410 245__ $$aThe status quo of automated cytoarchitecture analysis: Where are we, and where are we going?
001018410 260__ $$c2023
001018410 3367_ $$033$$2EndNote$$aConference Paper
001018410 3367_ $$2DataCite$$aOther
001018410 3367_ $$2BibTeX$$aINPROCEEDINGS
001018410 3367_ $$2DRIVER$$aconferenceObject
001018410 3367_ $$2ORCID$$aLECTURE_SPEECH
001018410 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1700743787_2156$$xAfter Call
001018410 520__ $$aCytoarchitectonic brain maps provide a microstructural reference for multi-modal human brain atlases, representing important indicators for brain connectivity and function. Cytoarchitectonic areas are defined by characteristic microstructural cell distributions, including the size, shape, type, orientation, and density of neurons, as well as their distinct laminar and columnar arrangement. High-resolution microscopic scans of histological human brain sections enable identifying cytoarchitectonic brain areas. Modern high-throughput microscopic scanners enable large-scale image acquisition, resulting in petabyte-scale microscopic imaging datasets that provide the foundation for next-generation brain atlases. As established cytoarchitectonic brain mapping methods based on statistical image analysis do not scale to such large datasets, ongoing research aims to develop methods for automatic classification and characterization of cytoarchitecture based on large amounts of high-resolution images.In this presentation, we will give an overview of the current state of automated cytoarchitecture analysis and provide an outlook on future developments in the field. We will discuss the roles, potentials, and challenges of supervised learning, self-supervised representation learning, and graph-based inference at whole-brain level in the context of cytoarchitecture analysis. Finally, we will comment on the potential impact of novel methods and technologies on the field, including zero-shot learning, data-driven cytoarchitectonic mapping, multi-modal latent space fusion, and exascale computing.
001018410 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001018410 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001018410 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001018410 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
001018410 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b1$$ufzj
001018410 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b2$$ufzj
001018410 909CO $$ooai:juser.fz-juelich.de:1018410$$pVDB
001018410 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
001018410 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b1$$kFZJ
001018410 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b2$$kFZJ
001018410 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001018410 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$$x1
001018410 9141_ $$y2023
001018410 920__ $$lyes
001018410 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001018410 980__ $$aconf
001018410 980__ $$aVDB
001018410 980__ $$aI:(DE-Juel1)INM-1-20090406
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