CAREER: Automated extraction of vessel data from images to construct new models for vascular networks in plants and animals

血液通过一个被称为心血管系统的高度分支和相互连接的网络从心脏泵入毛细血管，血管大小从微米到厘米不等，血流速度相差1000倍。类似类型的血管网络对于几乎所有多细胞生物的资源流动都是必不可少的，包括植物(木质部网络)、昆虫(气管网络)和哺乳动物。了解哪些进化原则和环境因素驱动了血管网络结构，以及是什么限制了血管网络结构的流动，这可能有助于更好地理解组织结构和功能，以及对森林、食物网甚至肿瘤等各种系统的影响。这项研究项目将提供直接测试现有模型和开发新的、更现实的模型所需的数据。产品将是用于从图像中提取血管数据的新软件(例如，磁共振成像(MRI)、计算机断层扫描(CT))、用于植物和动物血管测量的大型数据库、特定分类或组织共同的分支模式的识别，以及新理论。测量将包括血管半径、长度、分支比和分支角度。现有的模型与一些初步结果相矛盾，包括不对称的分支(两个不同大小和不同流量的子血管)和偏离自相似性(相似的分支模式在不同尺度上重复出现)。将构建新的模型来结合这些发现，并用于预测血管网络几何形状和流经血管网络的流量之间的联系。模型的一个关键预测将是描述毛细血管数量如何随网络容量变化的比例指数。与这些研究目标紧密结合的是三个教育目标：1)教学生如何使用图像识别软件从包括博物馆藏品和实验室在内的生物领域的图像中提取数据；2)教学生如何将经验结果转化为方程并测试具体的机械假说；3)通过大型、全面的数据库网站与公众和科学界共享数据，这将加速科学研究和社区推广。高中生、本科生、研究生以及博士后研究人员都将在这项研究中接受培训。值得注意的是，该项目每年将为三名高中生提供暑期研究经验。这种培训的一个关键方面是教学生如何将理论和实证数据结合起来，以及如何通过出版物、网站、课程材料、在专业会议和外展上的演讲来传播研究成果。PI有教育和培训各级学生的经验，并将积极从代表性不足的群体中招募学生从事信息学研究。出版物、数据库、代码和课程材料都将通过网站提供。总之，这项工作将提供一个例子，说明如何使用计算机视觉技术从世界各地的生物图像中提取数据。

Blood is pumped from the heart to the capillaries through a highly branched and interconnected network known as the cardiovascular system, with vessel sizes that range from microns to centimeters and blood flow speeds that differ by a factor of 1000. Similar types of vascular networks are essential for the flow of resources in nearly all multicellular organisms, including plants (xylem networks), insects (tracheal networks), and mammals. Understanding which evolutionary principles and environmental factors drive the structure of vascular networks, and what constrains the flow through them, could help lead to a better understanding of organismic structure and function with implications for systems as diverse as forests, food webs, and even tumors. This research project will supply the data needed to directly test existing models and to develop new, more realistic models. Outputs will be new software for extracting vascular data from images (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT)), a large database for vascular measurements in plants and animals, identification of branching patterns common to specific taxa or tissues, and new theory. Measurements will include vessel radii, lengths, branching ratios, and branching angles. Existing models are contradicted by some preliminary results, including asymmetric branching (two daughter vessels of different sizes and different flow) and deviations from self-similarity (similar branching patterns recurring across scales). New models will be constructed to incorporate these findings and used to predict connections between the geometry of and flow through vascular networks. A key prediction of models will be scaling exponents that describe how the number of capillaries changes with network volume. In an attempt to enable quick translation of branching geometry into predictions for flow rate, techniques will be adapted to classify vascular networks according to a suite of these characteristic scaling exponents.Closely integrated with these research objectives are three educational goals: 1) teach students how to use image recognition software to extract data from images across biological fields, including museum collections and labs, 2) teach students how to translate empirical results into equations and test specific, mechanistic hypotheses, and 3) motivate data sharing with the public and scientific community via websites for large, comprehensive databases that will accelerate scientific research and community outreach. High school, undergraduate, and graduate students, as well as postdoctoral researchers will all be trained during this research. Notably, summer research experiences for three high school students will be provided for each year of the project. A critical aspect of this training is in teaching students how to combine theory and empirical data and how to disseminate research findings through publications, websites, curricular materials, and talks at professional meetings as well as outreach. The PI has experience educating and training students at all levels and will actively recruit students from under-represented groups to engage in informatics research. Publications, databases, code, and curricular materials will all be made available through websites. Together, this work will provide an example of how computer vision techniques can be used to extract data from biological images around the world.