Building Geometric Databases for Anatomy-Based Spatial Queries

该基金旨在开发一个基于地图集的数据库架构，适合组织3D解剖图像进行有效的空间查询。利用一种新形式的图集，该项目将开发工具，用于准确和有效地对齐从大量具有不同解剖形状的受试者中收集的3D图像。由此产生的数据库将促进方便，基于网络的空间查询的功能，代谢，机械和结构数据的可定制的区域之间的存储图像的兴趣。作为一个试验平台的应用，该项目将构建两个基于网络的数据库的3D基因表达模式的小鼠大脑和心脏从堆栈的2D组织切片。在该架构的核心是一种新型的几何图谱，表示为细分网格，其模型的分割和内部的解剖区域的兴趣，如大脑。图谱的几何和多分辨率结构允许灵活地配准到具有不同解剖形状的个体受试者上，并且能够对配准数据进行快速、多级比较。用于构建、填充和查询基于地图集的数据库的工具将在网上免费提供，以便于与现有数据库集成，从而增强空间查询能力。生物成像在科学中的使用不断增长，因此，对于许多研究工作来说，能够有效地利用正在产生的巨大空间信息资源至关重要。该项目产生的基因表达数据库将能够基于3D表达模式的相似性对基因进行查询和聚类，这可能会在理解生物过程方面取得重大突破。该项目的长期目标是促进跨学科、图像模态和地理位置的协作知识发现。

This grant aims to develop an atlas-based database architecture suited for organizing 3D anatomical images for efficient spatial queries. Utilizing a new form of atlas, the project will develop tools for accurate and efficient alignment of 3D images collected from a large number of subjects with varying anatomical shapes. The resulting database will facilitate convenient, web-based spatial queries of functional, metabolic, mechanical, and structural data within customizable regions of interest among stored images. As a test-bed application, the project will construct two web-based databases of 3D gene expression patterns for the mouse brain and heart from stacks of 2D tissue sections.At the heart of the architecture is a new type of geometric atlas, represented as a subdivision mesh, which models the partitioning and interior of an anatomical region of interest, such as the brain. The geometric and multi-resolution structure of the atlas allows for flexible registration onto individual subjects with varying anatomical shapes, and enables fast, multi-level comparison of registered data. The tools for constructing, populating, and querying atlas-based databases will be made freely available online to be easily integrated with existing databases for augmented spatial querying capabilities.The use of biological imaging in science continues to grow, and it has thus become critical to many research efforts to be able to efficiently utilize the enormous resources of spatial information being generated. The gene expression databases resulted from the project will enable querying and clustering of genes based on similarity of 3D expression patterns, which may lead to significant breakthroughs in understanding biological processes. The long term goal of this project is to facilitate collaborative knowledge discovery across subjects, image modalities, and geographical locations.