Mayerich David M, Abbott Louise, Keyser John
Department of Computer Science, Texas A&M University.
IEEE Trans Vis Comput Graph. 2008 Nov-Dec;14(6):1611-8. doi: 10.1109/TVCG.2008.179.
Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.
了解生物组织中微血管结构的结构及其与细胞的关系是一个重要且复杂的问题。特别是脑微血管在慢性疾病中起着重要作用。然而，这些网络仅在微观层面可见，并且可以跨越大量组织。由于显微镜技术的最新进展，可以以重建这些结构所需的分辨率对大量数据进行成像。由于显微镜数据集密集且复杂的性质，限制显示的信息量很重要。在本文中，我们描述了编码微血管数据独特结构的方法，使研究人员能够有选择地探索微血管解剖结构。我们还确定了对研究微血管与细胞关系的研究人员最有用的查询。通过将细胞结构与我们的微血管框架相关联，我们使研究人员能够在密集和复杂的数据集中探索有趣的解剖关系。
