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000864572 1001_ $$0P:(DE-HGF)0$$aPallast, Niklas$$b0
000864572 245__ $$aAtlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto)
000864572 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2019
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000864572 520__ $$aCell counting in neuroscience is a routine method of utmost importance to support descriptive in vivo findings with quantitative data on the cellular level. Although known to be error- and bias-prone, manual cell counting of histological stained brain slices remains the gold standard in the field. While the manual approach is limited to small regions-of-interest in the brain, automated tools are needed to up-scale translational approaches and generate whole mouse brain counts in an atlas framework. Our goal was to develop an algorithm which requires no pre-training such as machine learning algorithms, only minimal user input, and adjustable variables to obtain reliable cell counting results for stitched mouse brain slices registered to a common atlas such as the Allen Mouse Brain atlas. We adapted filter banks to extract the maxima from round-shaped cell nuclei and various cell structures. In a qualitative as well as quantitative comparison to other tools and two expert raters, AIDAhisto provides accurate and fast results for cell nuclei as well as immunohistochemical stainings of various types of cells in the mouse brain.
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000864572 7001_ $$0P:(DE-HGF)0$$aWieters, Frederique$$b1
000864572 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon R.$$b2
000864572 7001_ $$0P:(DE-HGF)0$$aAswendt, Markus$$b3$$eCorresponding author
000864572 773__ $$0PERI:(DE-600)1500499-5$$a10.1016/j.jneumeth.2019.108394$$gp. S0165027019302511$$p108394$$tJournal of neuroscience methods$$v326$$x0165-0270$$y2019
000864572 8564_ $$uhttps://juser.fz-juelich.de/record/864572/files/Pallast_Post%20Print_2019_J%20Neurosci%20Meth_Atlas-based%20imaging%20data%20analysis%20tool%20for%20quantitative%20mouse%20brain%20histology.pdf$$yPublished on 2019-08-12. Available in OpenAccess from 2021-02-12.
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