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• W1966930384 abstract "The development of multi-photon imaging technique has greatly facilitated in vivo time-lapse imaging and enables comparison of the fine morphological structures of individual neurons over time. Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking. Manual analysis greatly limits the pace of research and introduces errors. Recent work suggests that an automated dynamic analysis tool can be successfully applied to time-lapse images of cultured neurons,1 which are flat and relatively simple. So far no such automated analysis program has yet been reported for in vivo 4D image analysis, which poses several technical challenges, including alignment of complex 3 dimensional structures across time points, and identification of persistent and dynamic structures.The goals of the Diadem Challenge were to generate algorithms for automated reconstruction of light microscope images of 3D neural structures. Quantitative characterization of neuronal structure will be extremely valuable for the identification of cell types, to establish and interpret neuronal connectivity maps, for comparative analysis across individuals, brain regions or experimental conditions, and to identify plasticity events and mechanisms that regulate plasticity, for instance by analysis of the structure of neurons from animals of different genotypes or with different experience or training. These goals are based on the premise that neuronal morphology is stereotypic, so that data from single reconstructions are meaningful within a dataset.Participants of the Diadem Challenge explored different model-based algorithms based on either local voxel information or global objective function of the image to automatically reconstruct the neuronal structure.2 The ultimate goal is to achieve completely automated reconstruction with high precision and efficiency. Progress demonstrated by participants of the Diadem Challenge indicates that the development of algorithms for automated reconstruction of neuronal structures is very promising and will be able to significantly facilitate supervised reconstruction of neurons in complex data sets. This progress is very exciting and will have a huge impact on current research in basic neuroscience and as it pertains to human disease.One important advance from this point will be to automate analysis of time-lapse imaging data. Applications of automated analysis of time-lapse imaging data include analysis of the development of neuronal structure and acquisition of cell type specific morphological features, analysis of mechanisms of structural plasticity, and events underlying degenerative and regenerative events." @default.
• W1966930384 created "2016-06-24" @default.
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• W1966930384 date "2011-01-27" @default.
• W1966930384 modified "2023-09-25" @default.
• W1966930384 title "Diadem X: Automated 4 Dimensional Analysis of Morphological Data" @default.
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• W1966930384 doi "https://doi.org/10.1007/s12021-011-9098-x" @default.
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