000888546 001__ 888546
000888546 005__ 20231123201912.0
000888546 0247_ $$2arXiv$$aarXiv:2011.12857
000888546 0247_ $$2Handle$$a2128/26378
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000888546 037__ $$aFZJ-2020-05010
000888546 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$eCorresponding author
000888546 245__ $$aConvolutional Neural Networks for cytoarchitectonic brain mapping at large scale
000888546 260__ $$c2020
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000888546 500__ $$aPreprint submitted to NeuroImage
000888546 520__ $$aHuman brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.
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000888546 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888546 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000888546 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x3
000888546 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x4
000888546 588__ $$aDataset connected to arXivarXiv
000888546 7001_ $$0P:(DE-Juel1)167110$$aSpitzer, Hannah$$b1
000888546 7001_ $$0P:(DE-Juel1)171890$$aKiwitz, Kai$$b2
000888546 7001_ $$0P:(DE-Juel1)171533$$aUnger, Nina$$b3
000888546 7001_ $$0P:(DE-HGF)0$$aWagstyl, Konrad$$b4
000888546 7001_ $$0P:(DE-HGF)0$$aEvans, Alan C.$$b5
000888546 7001_ $$0P:(DE-HGF)0$$aHarmeling, Stefan$$b6
000888546 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b7
000888546 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b8
000888546 8564_ $$uhttps://juser.fz-juelich.de/record/888546/files/Schiffer_etal_bioRXiv_2020_prepint.pdf$$yOpenAccess
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000888546 9141_ $$y2020
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000888546 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
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