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• W2905361879 abstract "Wide-field microscopes are commonly used in neurobiology for experimental studies of brain samples. Available visualization tools are limited to electron, two-photon, and confocal microscopy datasets, and current volume rendering techniques do not yield effective results when used with wide-field data. We present a workflow for the visualization of neuronal structures in wide-field microscopy images of brain samples. We introduce a novel gradient-based distance transform that overcomes the out-of-focus blur caused by the inherent design of wide-field microscopes. This is followed by the extraction of the 3D structure of neurites using a multi-scale curvilinear filter and cell-bodies using a Hessian-based enhancement filter. The response from these filters is then applied as an opacity map to the raw data. Based on the visualization challenges faced by domain experts, our workflow provides multiple rendering modes to enable qualitative analysis of neuronal structures, which includes separation of cell-bodies from neurites and an intensity-based classification of the structures. Additionally, we evaluate our visualization results against both a standard image processing deconvolution technique and a confocal microscopy image of the same specimen. We show that our method is significantly faster and requires less computational resources, while producing high quality visualizations. We deploy our workflow in an immersive gigapixel facility as a paradigm for the processing and visualization of large, high-resolution, wide-field microscopy brain datasets." @default.
• W2905361879 created "2018-12-22" @default.
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• W2905361879 date "2019-01-01" @default.
• W2905361879 modified "2023-10-16" @default.
• W2905361879 title "Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images" @default.
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• W2905361879 doi "https://doi.org/10.1109/tvcg.2018.2864852" @default.
• W2905361879 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6382602" @default.
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• W2905361879 hasPublicationYear "2019" @default.
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