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首页> 外文会议>International Conference on Bioinformatics and Computational Biology >Three-Dimensional Multimodality Modelling by Integration of High-Resolution Interindividual Atlases and Functional MALDI-IMS Data
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Three-Dimensional Multimodality Modelling by Integration of High-Resolution Interindividual Atlases and Functional MALDI-IMS Data

机译：高分辨率与Maldi-IMS数据集成的三维多模建模

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摘要

We present an approach for the analysis of phenotypic diversity in morphology and internal composition of biological specimen by means of high resolution 3-D models of developing barley grains. Three-dimensional histological structures are resolved by reconstructing specimen from large stacks of serially sectioned material, which is a preliminary for the spatial assignment of key tissues in differentiation. By sampling and constructing models at different developmental time steps from multiple individuals, we address two aims in a computational phenomics context: i) Generation of averaging atlases as structural references for integration of functional data, and ii) building the basis for a mathematical model of grain morphogenesis. We have established an algorithmic pipeline for automated processing of large image stacks towards phenotypic 3-D models and data-integration, comprising registration, multi-label segmentation, and alignment of functional measurements. The described algorithms allow high-throughput reconstruction and tissue recognition of datasets comprising thousands of images. The usefulness of the approach is demonstrated by automated model generation, allowing volumetric measurements of tissue composition, three-dimensional analysis of diversity, and the integration of MALDI-IMS data by mutual information based registration, which is a significant contribution to a systematic analysis of differentiation and development.
机译：我们通过高分辨率3-D模型进行了大麦谷物的高分辨率3-D模型，提出了一种分析生物样本形态和内部组成的表型多样性的方法。通过从大堆叠的连续切片材料重建样品来解决三维组织学结构，这是分化中关键组织的空间分配的初步。通过从多个人的不同发展时间步骤进行采样和构建模型，我们在计算表情背景下解决了两个目标：i）生成作为集成功能数据集成的结构参考的生成，以及构建数学模型的基础晶粒形态发生。我们已经建立了一种用于自动处理大型图像堆栈的算法流水线，朝向表型3-D模型和数据集成，包括注册，多标签分割和功能测量的对准。所描述的算法允许高吞吐量重建和组织识别包括数千个图像的数据集。通过自动模型生成证明了该方法的有用性，允许组织成分的体积测量，多样性的三维分析，以及通过基于相互信息的登记的MALDI-IMS数据的整合，这是对系统分析的重要贡献分化与发展。

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