001048785 001__ 1048785
001048785 005__ 20251204202145.0
001048785 037__ $$aFZJ-2025-04900
001048785 041__ $$aEnglish
001048785 1001_ $$0P:(DE-Juel1)138708$$aHuysegoms, Marcel$$b0$$eCorresponding author$$ufzj
001048785 1112_ $$a9th BigBrain Workshop - HIBALL Closing Symposium$$cBerlin$$d2025-10-27 - 2025-10-29$$wGermany
001048785 245__ $$aTowards a Cellular 3D Model of the Human Brain at 1µm Isotropic Resolution
001048785 260__ $$c2025
001048785 3367_ $$033$$2EndNote$$aConference Paper
001048785 3367_ $$2BibTeX$$aINPROCEEDINGS
001048785 3367_ $$2DRIVER$$aconferenceObject
001048785 3367_ $$2ORCID$$aCONFERENCE_POSTER
001048785 3367_ $$2DataCite$$aOutput Types/Conference Poster
001048785 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1764842346_5626$$xAfter Call
001048785 520__ $$a<b>Background:</b> 3D histological reconstruction is well established at mesoscopic scales (~20µm). At this resolution, the task can be framed as an optimization of spatial transformations driven by pixel-level similarity metrics, typically within coarse-to-fine reconstruction frameworks that integrate both slice-to-slice and histology-to-reference registrations (Amunts et al., 2013; Alkemade et al., 2022).<br><Pushing toward cellular precision (<5µm) introduces several key challenges. First, intensity-based matching becomes unreliable: individual dark features often represent different cells across consecutive sections, so intensities do not correspond one-to-one. Moreover, staining variability and tissue distortions further amplify noise. Second, reference volumes (e.g. MRI or blockface) lack the necessary spatial detail to constrain alignment at the microstructural level. In the absence of a reliable reference volume, sequential reconstruction alone is prone to cumulative alignment drift (commonly known as the “banana effect”), which can artificially straighten curved anatomical structures.<br>We aimed to address these challenges through the development of a novel framework for 3D reconstruction of histological sections that accurately represents the original volume using a three-step feature-matching and global optimization strategy.<br><br><b>Methods:</b> Optical z-scanning enables imaging of twenty depths within each 20 µm thick histological section by gradually shifting the focal plane, resulting in a 1 µm isotropic resolution volume per section. The principal challenge then lies in accurately reconstructing the sections to achieve a faithful representation of the anatomy of the unsliced brain. Our approach involves (i) computing point correspondences between neighbouring sections (e.g. bisected cells or blood vessels). Then, (ii) optimize a global cost function to jointly estimate the spatial coordinates of matched features across all sections, enforcing both matching and smoothness constraints. Finally, (iii) to produce the final volume, polyaffine transformations are fitted to warp each histological section onto the optimized feature positions.<br><br><b>Results:</b> We compared our global reconstruction approach against several other strategies, demonstrating its potential to outperform standard sequential pipelines in terms of alignment accuracy, structural smoothness, and anatomical preservation. The final reconstructed volume maintains natural cortical curvature, resolves laminar organization throughout the cortex (even in oblique cutting planes) and preserves overall brain shape.<br><br><b>Conclusion:</b> Our framework provides a robust, scalable foundation for building cellular-resolution 3D models of the human brain. It makes minimal assumptions about feature types, accommodating both traditional descriptors (e.g., SURF) and ML-based detectors (e.g., LightGlue). The method also generalizes across different tissue stainings and section thicknesses (10–60 µm). Although the approach can operate without a reference volume, one can be incorporated to better preserve overall shape in cases of substantial nonlinear tissue deformation. Future work will generate detailed, region-specific descriptions of cellular organization, which can in turn support realistic simulations of human brain activity.
001048785 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048785 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001048785 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001048785 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$$x3
001048785 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x4
001048785 7001_ $$0P:(DE-Juel1)171152$$aKropp, Jan-Oliver$$b1$$ufzj
001048785 7001_ $$0P:(DE-Juel1)174282$$aWenzel, Susanne$$b2$$ufzj
001048785 7001_ $$0P:(DE-Juel1)7935$$aOliveira, Sarah$$b3$$ufzj
001048785 7001_ $$0P:(DE-Juel1)187055$$aPaquola, Casey$$b4$$ufzj
001048785 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b5$$ufzj
001048785 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b6$$ufzj
001048785 8564_ $$uhttps://events.hifis.net/event/2171/contributions/20178/
001048785 909CO $$ooai:juser.fz-juelich.de:1048785$$popenaire$$pVDB$$pec_fundedresources
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138708$$aForschungszentrum Jülich$$b0$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171152$$aForschungszentrum Jülich$$b1$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174282$$aForschungszentrum Jülich$$b2$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)7935$$aForschungszentrum Jülich$$b3$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187055$$aForschungszentrum Jülich$$b4$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b5$$kFZJ
001048785 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b6$$kFZJ
001048785 9131_ $$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
001048785 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$$x1
001048785 9141_ $$y2025
001048785 920__ $$lyes
001048785 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001048785 980__ $$aposter
001048785 980__ $$aVDB
001048785 980__ $$aI:(DE-Juel1)INM-1-20090406
001048785 980__ $$aUNRESTRICTED