000824919 001__ 824919 000824919 005__ 20210129225144.0 000824919 020__ $$a978-3-319-50861-0 (print) 000824919 020__ $$a978-3-319-50862-7 (electronic) 000824919 0247_ $$2doi$$a10.1007/978-3-319-50862-7_3 000824919 0247_ $$2ISSN$$a0302-9743 000824919 0247_ $$2ISSN$$a1611-3349 000824919 0247_ $$2altmetric$$aaltmetric:21831789 000824919 037__ $$aFZJ-2016-07420 000824919 041__ $$aEnglish 000824919 082__ $$a004 000824919 1001_ $$0P:(DE-Juel1)132291$$aLührs, Anna$$b0$$eCorresponding author$$ufzj 000824919 1112_ $$aInternational Workshop on Brain-Inspired Computing$$cCetraro$$d2015-07-06 - 2015-07-10$$gBrainComp 2015$$wItaly 000824919 245__ $$aTowards Large-Scale Fiber Orientation Models of the Brain – Automation and Parallelization of a Seeded Region Growing Segmentation of High-Resolution Brain Section Images 000824919 260__ $$aCham$$bSpringer International Publishing$$c2016 000824919 29510 $$aBrain-Inspired Computing 000824919 300__ $$a28 - 42 000824919 3367_ $$2ORCID$$aCONFERENCE_PAPER 000824919 3367_ $$033$$2EndNote$$aConference Paper 000824919 3367_ $$2BibTeX$$aINPROCEEDINGS 000824919 3367_ $$2DRIVER$$aconferenceObject 000824919 3367_ $$2DataCite$$aOutput Types/Conference Paper 000824919 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1563262675_3032 000824919 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb 000824919 4900_ $$aLecture Notes in Computer Science$$v10087 000824919 520__ $$aTo understand the microscopical organization of the human brain including cellular and fiber architectures, it is a necessary prerequisite to build virtual models of the brain on a sound biological basis. 3D Polarized Light Imaging (3D-PLI) provides a window to analyze the fiber architecture and the fibers’ intricate inter-connections at microscopic resolutions. Considering the complexity and the pure size of the human brain with its nearly 86 billion nerve cells, 3D-PLI is challenging with respect to data handling and analysis in the TeraByte to PetaByte ranges, and inevitably requires supercomputing facilities. Parallelization and automation of image processing steps open up new perspectives to speed up the generation of new high resolution models of the human brain to provide groundbreaking insights into the brain’s three-dimensional micro architecture. Here, we will describe the implementation and the performance of a parallelized semi-automated seeded region growing algorithm used to classify tissue and background components in up to one million 3D-PLI images acquired from an entire human brain. This algorithm represents an important element of a complex UNICORE-based analysis workflow ultimately aiming at the extraction of spatial fiber orientations from 3D-PLI measurements. 000824919 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0 000824919 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1 000824919 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2 000824919 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x3 000824919 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x4 000824919 588__ $$aDataset connected to CrossRef Book Series 000824919 7001_ $$0P:(DE-Juel1)132074$$aBücker, Oliver$$b1$$ufzj 000824919 7001_ $$0P:(DE-Juel1)131632$$aAxer, Markus$$b2$$ufzj 000824919 773__ $$a10.1007/978-3-319-50862-7_3 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.pdf$$yRestricted 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.gif?subformat=icon$$xicon$$yRestricted 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.jpg?subformat=icon-1440$$xicon-1440$$yRestricted 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.jpg?subformat=icon-180$$xicon-180$$yRestricted 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.jpg?subformat=icon-640$$xicon-640$$yRestricted 000824919 8564_ $$uhttps://juser.fz-juelich.de/record/824919/files/BrainComp2015-Luehrs.pdf?subformat=pdfa$$xpdfa$$yRestricted 000824919 909CO $$ooai:juser.fz-juelich.de:824919$$pec_fundedresources$$pVDB$$popenaire 000824919 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132291$$aForschungszentrum Jülich$$b0$$kFZJ 000824919 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132074$$aForschungszentrum Jülich$$b1$$kFZJ 000824919 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131632$$aForschungszentrum Jülich$$b2$$kFZJ 000824919 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0 000824919 9131_ $$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$$x1 000824919 9141_ $$y2016 000824919 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz 000824919 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS 000824919 920__ $$lyes 000824919 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 000824919 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x1 000824919 980__ $$acontrib 000824919 980__ $$aVDB 000824919 980__ $$acontb 000824919 980__ $$aI:(DE-Juel1)JSC-20090406 000824919 980__ $$aI:(DE-Juel1)INM-1-20090406 000824919 980__ $$aUNRESTRICTED 000824919 981__ $$aI:(DE-Juel1)INM-1-20090406