Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images
| Autor: | Arie E. Kaufman, David A. Talmage, Mala Ananth, Shreeraj Jadhav, Lorna W. Role, Saeed Boorboor |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Volume rendering Computer Graphics and Computer-Aided Design Article Rendering (computer graphics) Visualization Data visualization Signal Processing Microscopy Computer vision Computer Vision and Pattern Recognition Artificial intelligence Deconvolution business Distance transform Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 25:1018-1028 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2018.2864852 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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