001043525 001__ 1043525
001043525 005__ 20250716202229.0
001043525 037__ $$aFZJ-2025-02901
001043525 041__ $$aEnglish
001043525 1001_ $$0P:(DE-Juel1)184804$$aOberstraß, Alexander$$b0$$eCorresponding author$$ufzj
001043525 1112_ $$aHelmholtz AI Conference 2025$$cKarlsruhe$$d2025-06-03 - 2025-06-05$$gHAICON25$$wGermany
001043525 245__ $$aImage-to-Image Translation for Virtual Cresyl Violet Staining From 3D Polarized Light Imaging
001043525 260__ $$c2025
001043525 3367_ $$033$$2EndNote$$aConference Paper
001043525 3367_ $$2BibTeX$$aINPROCEEDINGS
001043525 3367_ $$2DRIVER$$aconferenceObject
001043525 3367_ $$2ORCID$$aCONFERENCE_POSTER
001043525 3367_ $$2DataCite$$aOutput Types/Conference Poster
001043525 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1752680447_22200$$xAfter Call
001043525 520__ $$aCharacterizing the structure of cortical networks in the brain requires complementary imaging techniques and the integration of different aspects such as fiber and cell body distributions. Ideally, different methods are applied to the same tissue for direct comparison. 3D polarized light imaging (3D-PLI) visualizes nerve fibers in brain tissue at high resolution based on optical properties alone. This enables subsequent staining of the same tissue for cell bodies after 3D-PLI measurement. However, this process is time-consuming, technically challenging, and requires nonlinear cross-modal registration to obtain pixel correspondence.Here we investigate image-to-image translation methods to predict the results of cell body staining directly from 3D-PLI, using generative adversarial networks (GANs) and neural style transfer (NST). We use 11 coronal sections of a vervet monkey brain for training, each imaged with 3D-PLI and subsequently stained with Cresyl violet for cell bodies. Since pixel-accurate registration of entire sections may be difficult and error-prone, we introduce an online registration head to linearly align model predictions for local image patches to the post-staining during network training. This exploits the fact that local deformations can be approximated by a linear model when a coarse pre-registration is available. The online alignment improves with predictions during training and ultimately converges to an accurate registration. We use a Fourier-based registration approach that is computationally efficient and GPU-parallelizable.We quantify model performance by comparing the predicted virtual staining to post-staining after 3D-PLI measurement. Our best model localizes the majority of larger cell instances (>100 µm² in-plane) segmented by a contour proposal network (CPN) with an F1 score of 63.1. The proposed online registration head significantly improves the performance of all investigated models, increasing F1 scores from 40.6 to 63.1 for NST and from 22.2 to 50.3 for a GAN.The applied virtual staining enables automatic localization of larger cell instances in unstained 3D-PLI images. Since the model predictions are pixel-aligned with 3D-PLI, they enable joint analysis of fiber tracts and cell bodies and may also serve as targets for registration of real post-staining. Future work will extend the training data to include more sections, brains and species, with potential applications to other imaging modalities.
001043525 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001043525 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x1
001043525 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x2
001043525 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$$x3
001043525 536__ $$0G:(GEPRIS)313856816$$aDFG project G:(GEPRIS)313856816 - SPP 2041: Computational Connectomics (313856816)$$c313856816$$x4
001043525 536__ $$0G:(BMBF)01GQ1902$$a3D-MMA - Gradienten der Verteilung multipler Transmitterrezeptoren in der Hirnrinde als Grundlage verteilter kognitiver, sensorischer und motorischer Funktionen. (01GQ1902)$$c01GQ1902$$x5
001043525 7001_ $$0P:(DE-Juel1)186746$$aVaca Cerda, Esteban Alejandro$$b1$$ufzj
001043525 7001_ $$0P:(DE-Juel1)177675$$aUpschulte, Eric$$b2$$ufzj
001043525 7001_ $$0P:(DE-Juel1)171512$$aNiu, Meiqi$$b3$$ufzj
001043525 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b4$$ufzj
001043525 7001_ $$0P:(DE-Juel1)131642$$aGrässel, David$$b5$$ufzj
001043525 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b6$$ufzj
001043525 7001_ $$0P:(DE-Juel1)131632$$aAxer, Markus$$b7$$ufzj
001043525 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b8$$ufzj
001043525 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b9$$ufzj
001043525 909CO $$ooai:juser.fz-juelich.de:1043525$$popenaire$$pVDB$$pec_fundedresources
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184804$$aForschungszentrum Jülich$$b0$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186746$$aForschungszentrum Jülich$$b1$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177675$$aForschungszentrum Jülich$$b2$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171512$$aForschungszentrum Jülich$$b3$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b4$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131642$$aForschungszentrum Jülich$$b5$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b6$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131632$$aForschungszentrum Jülich$$b7$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b8$$kFZJ
001043525 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b9$$kFZJ
001043525 9131_ $$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
001043525 9141_ $$y2025
001043525 920__ $$lyes
001043525 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001043525 980__ $$aposter
001043525 980__ $$aVDB
001043525 980__ $$aI:(DE-Juel1)INM-1-20090406
001043525 980__ $$aUNRESTRICTED