Towards semantically steered navigation in shape spaces exemplified by rodent skull morphology in correlation to external attributes

形态计量学在生物学中的一个主要任务是发现遗传、地理和生态数据等外在属性与一类生物体的特定形状之间的相关性。这需要用于将数据分析结果从属性空间映射到形状空间以及从形状空间映射到属性空间的技术，从而允许用户在交互式反馈回路内在形状空间以及属性空间中细化数据分析。该项目建立在计算机科学和生物学之间已经存在的研究合作的基础上，其中我们为啮齿动物下颌骨的CT数据建立了线性形状空间。与最先进的体可视化技术一起，将属性空间中的分析结果映射到形状空间，允许沿着形状空间中有意义的方向（特征）进行交互式导航。在这个项目中，我们想把这个研究扩展到四个方面。首先，将量化使用当前系统的导航技术检测到的形状变化，以用于遗传作图，从而能够进行形状和遗传因素的相关分析。其次，我们的目标是一个分层的形状模型，允许分离粗和细尺度形状变化。第三，为了使用户能够将人类知识融入分析过程，我们计划集成单个形状的交互式变形技术。这将允许沿着形状空间中的相应方向导航，并使用结果来细化属性空间中的分析。此外，可以通过编辑的形状有目的地扩展形状空间。最后但并非最不重要的是，我们计划加强形状空间分析从刚性到可变形的形状。为此，我们包括啮齿动物的尾巴作为额外的重要数据源的相关性分析和计划应用频谱嵌入方法来提出一个等距不变的形状表示。

A major task of morphometrics in biology is to discover correlations between extrinsic attributes like genetic, geographic and ecological data and the specific shape of a class of organisms. This requires techniques for mapping data analysis results from attribute space to shape space and vice versa allowing the user to refine the data analysis in shape space as well as in attribute space within an interactive feedback loop. This project builds on an already existing research cooperation between Computer Science and Biology in which we established a linear shape space for CT data of rodent mandibles. Together with state-of-the-art volume visualization techniques the mapping of analysis results in attribute space to shape space allows the interactive navigation along meaningful directions (traits) in shape space. In this project we want to extend this research towards four aspects. First of all shape variations detected using the navigation techniques of the current system will be quantified for the purpose of genetic mapping enabling correlation analysis of shape and genetic factors. Second we aim for a hierarchical shape model that allows to separate coarse and fine scale shape variations. Third, to enable the user to incorporate human knowledge into the analysis process we plan to integrate interactive deformation techniques of individual shapes. This will allow navigating along corresponding directions in shape space and using the results to refine the analysis in attribute space. In addition, the shape space can be extended purposefully by the edited shape. Last but not least we plan to enhance the shape space analysis from rigid to deformable shapes. For this aim we include rodent tails as additional important data source for correlation analysis and plan to apply spectral embedding methods to come up with an isometric invariant shape representation.