Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images.
Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images.
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DOI：
10.1109/tvcg.2018.2864852
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发表时间：
2018-08-20
影响因子：
5.2
通讯作者：
Kaufman A
中科院分区：
文献类型：
--
作者：
Boorboor S;Jadhav;Ananth M;Talmage D;Role;Kaufman A
宽视场显微镜通常用于神经生物学中对大脑样本的实验研究。可用的可视化工具仅限于电子，双光子和共焦显微镜数据集，目前的体绘制技术不产生有效的结果时，使用宽场数据。我们提出了一个工作流程，用于脑样本的宽视野显微镜图像中的神经元结构的可视化。我们引入了一种新的基于梯度的距离变换，克服了由宽视场显微镜的固有设计所造成的失焦模糊。随后使用多尺度曲线滤波器提取神经突的3D结构，并使用基于Hessian的增强滤波器提取细胞体。然后将这些过滤器的响应作为不透明度贴图应用于原始数据。基于领域专家所面临的可视化挑战，我们的工作流程提供了多种渲染模式，以实现神经元结构的定性分析，其中包括从神经突分离细胞体和基于强度的结构分类。此外，我们评估我们的可视化结果对一个标准的图像处理反卷积技术和同一标本的共聚焦显微镜图像。我们表明，我们的方法是显着更快，需要更少的计算资源，同时产生高质量的可视化。我们将我们的工作流程部署在一个身临其境的千兆像素设施中，作为大型，高分辨率，宽视野显微镜大脑数据集的处理和可视化的范例。
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.