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COLLABORATIVE RESEARCH: ABI Innovation: Shape Analysis for Phenomics with 3D Imaging Data

合作研究：ABI Innovation：利用 3D 成像数据进行表型组学形状分析

基本信息

批准号：
1147260
负责人：
Lawrence Frank
金额：
$ 129.11万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2018-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147260&HistoricalAwards=false
关键词：
COLLABORATIVE RESEARCH ABI Innovation Shape

项目摘要

The field of comparative morphology has been revolutionized by the application of high resolution digital imaging methods to non-invasively visualize complex physiological features. However, truly quantitative characterization and comparison of complex morphological features still cannot be adequately addressed by existing methods, which are typically developed only for idealized 2D images or surfaces. The accurate and efficient characterization and comparison of shapes with 3D noisy imaging data represents highly non-trivial computational problems which have yet to be adequately addressed in comparative morphology studies, nor has efficient computational software been made available to the researchers. The goal of this project is to develop advanced computational methods for accurate quantitative characterization and comparison of specimen morphology from high resolution 3D voxel-based digital imaging modalities. With the growing recognition of the importance of digital libraries of biological specimens derived from advanced imaging methods, such as the NSF funded Digital Fish Library (DFL) and Digital Morphology (DigiMorph) projects, advanced methods for utilizing these data are of great importance, but pose significant technical challenges. Two broad classes of problems are of critical importance: 1) The ability to quantitatively characterize complicated morphological features from high resolution volumetric data and 2) Methods for comparing such features between specimens. Our objective is to develop two specific computational methods for geometric morphological analysis that can optimally characterize and compare geometric features embedded within real 3D imaging data, and are robust to noise and resolution limitations: 1) A novel shape analysis method based on signatures derived from spherical wave decomposition of 3D images; 2) A robust non-linear spatial normalization method based on diffeomorphic image and landmark registration. The spatial normalization methods will allow homologous structures to be correctly non-linearly warped to each other or a common template for comparison, while the decomposition method will facilitate robust, efficient, accurate, and automated characterization of shapes embedded within complex 3D datasets. These methods can then be used to generate species-specific atlases that define normative morphologies, thus facilitating both inter- and intra-specific comparative analyses. These methods will then be applied to the general problem of automated shape segmentation, then tested on two problems of significant biological importance: 1) Co-evolution of the short-tailed opossum inner ear and cranium and 2) Three-spine stickleback evolution from freshwater to saltwater species.Characterization and comparison of morphological (form, shape, or structure) variations is a problem of significant impact across a wide range of biological disciplines. The rise of 3D volumetric imaging methods for digitizing biological samples offers great possibilities for addressing these issues but requires a theoretical and computational framework capable of allowing researchers efficient and accurate methods for analyzing complicated biological structures embedded within 3D volumetric noisy digital data. The goal of this project is to develop computational tools to address the two primary issues at the heart of these analyses: The ability to accurately and efficiently 1) characterize complex morphological features and 2) compare morphological features between specimens. The ability to perform these is critical to facilitating the use of all digital library data for quantitative morphology but to date have not been developed. The goal of this proposal is to develop analysis software to fill this significantgap in the bridge between digital imaging methods and its ultimate potential for transforming the field of comparative morphology by developing computational methods to address a broad range of morphological questions that inform our knowledge of the evolution and diversification of species. The methods developed by this project will greatly extend the capabilities of researchers and students to incorporate quantitative anatomical measurements into the study of evolutionary biology. The methods are general and applicable to any 3D imaging modality and thus will be of utility to any digital library and will serve as a platform on which new technologies and methodologies can be applied in the future. The resulting analysis tools will be open source and disseminated to researchers through the DFL website (http://www.digitalfishlibrary.org). A corresponding public education exhibit will be developed at the Cabrillo Marine Aquarium (http://www.cabrillomarineaquarium.org/). Developing a general computational platform for the computational morphology from 3D digital data will allow evolutionary biologists to quantitatively and reproducibly address problems that provide greater insight into how ecological parameters might be, quite literally, 'shaping' biodiversity and thus has potentially profound implications for the field of evolutionary biology.

随着高分辨率数字成像方法的应用，比较形态学领域发生了革命性的变化，可以非侵入性地显示复杂的生理特征。然而，现有的方法仍然不能很好地解决复杂形态特征的真正定量表征和比较，这些方法通常只针对理想化的2D图像或表面而开发。准确而高效地描述和比较形状与3D噪声成像数据代表着高度不平凡的计算问题，这些问题在比较形态学研究中尚未得到充分解决，也没有有效的计算软件可供研究人员使用。该项目的目标是开发先进的计算方法，用于准确地定量描述和比较基于高分辨率3D体素的数字成像模式的样本形态。随着人们越来越多地认识到生物标本数字图书馆的重要性，这些数字图书馆源自先进的成像方法，如NSF资助的数字鱼类图书馆(DFL)和数字形态(DigiMorph)项目，利用这些数据的先进方法非常重要，但构成了重大的技术挑战。两大类问题至关重要：1)从高分辨率体积数据中定量描述复杂形态特征的能力；2)在样本之间比较这种特征的方法。我们的目标是开发两种几何形态分析的具体计算方法，能够最优地刻画和比较真实3D成像数据中嵌入的几何特征，并且对噪声和分辨率限制具有鲁棒性：1)基于3D图像球面波分解的特征的形状分析方法；2)基于微分同胚图像和地标配准的稳健的非线性空间归一化方法。空间归一化方法将允许同源结构被正确地非线性地扭曲到彼此或用于比较的公共模板，而分解方法将促进嵌入复杂3D数据集中的形状的稳健、高效、准确和自动化的表征。然后可以使用这些方法生成特定物种的地图集，定义标准的形态，从而促进种间和种内的比较分析。这些方法将被应用于自动形状分割的一般问题，然后在两个具有重要生物学意义的问题上进行测试：1)短尾负鼠内耳和头盖骨的共同进化；2)三刺鱼从淡水物种到咸水物种的进化。形态(形状、形状或结构)变化的特征和比较是一个跨越广泛生物学学科的重大影响问题。用于数字化生物样本的3D体积成像方法的兴起为解决这些问题提供了巨大的可能性，但需要一个理论和计算框架，使研究人员能够高效而准确地分析嵌入3D体积噪声数字数据中的复杂生物结构。该项目的目标是开发计算工具，以解决这些分析的核心两个主要问题：1)准确和有效地表征复杂的形态特征，2)比较标本之间的形态特征。执行这些操作的能力对于促进使用所有数字图书馆数据进行定量形态学是至关重要的，但迄今尚未开发。这一提议的目标是开发分析软件，通过开发计算方法来解决广泛的形态问题，从而填补数字成像方法与其转变比较形态领域的最终潜力之间的这一重大差距，这些问题为我们提供了关于物种进化和多样性的知识。该项目开发的方法将极大地扩展研究人员和学生将定量解剖学测量纳入进化生物学研究的能力。这些方法是通用的，适用于任何3D成像方式，因此将对任何数字图书馆有用，并将作为未来可以应用新技术和方法的平台。由此产生的分析工具将是开源的，并通过DFL网站(http://www.digitalfishlibrary.org).)分发给研究人员将在卡布里洛海洋水族馆(http://www.cabrillomarineaquarium.org/).)开展相应的公众教育展览根据3D数字数据开发计算形态的通用计算平台将使进化生物学家能够定量和可重复地解决问题，这些问题提供了更深入的见解，确切地说，生态参数可能如何‘塑造’生物多样性，因此对进化生物学领域具有潜在的深远影响。