000877613 001__ 877613
000877613 005__ 20220930130243.0
000877613 0247_ $$2Handle$$a2128/25327
000877613 0247_ $$2URN$$aurn:nbn:de:0001-2020072216
000877613 0247_ $$2ISSN$$a1866-1807
000877613 020__ $$a978-3-95806-469-0
000877613 037__ $$aFZJ-2020-02328
000877613 041__ $$aEnglish
000877613 1001_ $$0P:(DE-Juel1)167110$$aSpitzer, Hannah$$b0$$eCorresponding author$$gfemale$$ufzj
000877613 245__ $$aAutomatic Analysis of Cortical Areas in Whole Brain Histological Sections using Convolutional Neural Networks$$f- 2020-07-22
000877613 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000877613 300__ $$aXII, 162 S.
000877613 3367_ $$2DataCite$$aOutput Types/Dissertation
000877613 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$mbook
000877613 3367_ $$2ORCID$$aDISSERTATION
000877613 3367_ $$2BibTeX$$aPHDTHESIS
000877613 3367_ $$02$$2EndNote$$aThesis
000877613 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1595399610_1261
000877613 3367_ $$2DRIVER$$adoctoralThesis
000877613 4900_ $$aSchriften des Forschungszentrums Jülich. Reihe Schlüsseltechnologien / Key Technologies$$v218
000877613 502__ $$aUniversität Düsseldorf, Diss., 2020$$bDr.$$cUniversität Düsseldorf$$d2020
000877613 520__ $$aThe segregation of the human brain in cytoarchitectonic areas is an important prerequisite for the allocation of functional imaging, physiological, connectivity, molecular and genetic data to structurally well-defined entities of the human brain. Cytoarchitecture describes the spatial distribution of cell bodies and their shape and size, and is most appropriately studied at microscopic resolution based on cell-body stained histological sections. To determine boundaries between cytoarchitectonic areas, an observer-independent method that uses image analysis and multivariate statistical tools to capture changes in the distribution of cell bodies is already established. Nowadays, new technologies for high-throughput microscopy allow rapid digitization of histological sections, which increases the need for a fully automatic brain area segmentation method. This task is extremely challenging due to the high interindividual variability in cortical folding, sectioning artifacts, limited labeled training data, and the need for large input sizes for automatic methods. This work shows that convolutional neural networks, a special class of deep artificial neural networks, are suitable for automatic brain area segmentation. It introduces a semantic segmentation model that combines texture input given by high-resolution extracts of the histological sections with prior knowledge given by an existing probabilistic brain area atlas, the JuBrain atlas. This atlas prior helps the model to localize the texture input in the brain and allows it to make topologically correct brain area predictions. To overcome the limited amount of brain area annotations, the model can be pre-trained on a modified task for which training data is easier to obtain. Pre-training the model on a self-supervised task based on predicting the spatial distance between image patches extracted from sections of the same brain significantly increases the segmentation performance and enables the prediction of several brain areas in previously unseen brains. The self-supervised model learns a compact internal feature representation of the input using the inherent structure of the brain, without having explicit access to the concept of brain areas. Extensive evaluations indicate that these features encode cytoarchitectonic properties. This is remarkable result which allows the data-driven analysis of the structure of the entire brain. Although the presented model is not yet robust enough for automatic annotation of all areas in complete human brains, it is already leveraged for practical use by training specialized multi-scale models to propagate brain area labels from annotated sections to spatially close sections. This workflow has the potential to speed up current brain mapping projects by reducing the workload of the neuroscientists and produces previously unattainable high-resolution 3D views of single brain areas.
000877613 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000877613 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x1
000877613 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000877613 8564_ $$uhttps://juser.fz-juelich.de/record/877613/files/Schluesseltech_218.pdf$$yOpenAccess
000877613 8564_ $$uhttps://juser.fz-juelich.de/record/877613/files/Schluesseltech_218.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000877613 909CO $$ooai:juser.fz-juelich.de:877613$$pec_fundedresources$$pVDB$$pdriver$$purn$$popen_access$$popenaire$$pdnbdelivery
000877613 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000877613 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000877613 9141_ $$y2020
000877613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167110$$aForschungszentrum Jülich$$b0$$kFZJ
000877613 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000877613 920__ $$lyes
000877613 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000877613 980__ $$aphd
000877613 980__ $$aVDB
000877613 980__ $$aUNRESTRICTED
000877613 980__ $$abook
000877613 980__ $$aI:(DE-Juel1)INM-1-20090406
000877613 9801_ $$aFullTexts