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首页> 外文会议>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：高分辨率个体图集与功能性MALDI-IMS数据集成的三维多模态建模

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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：我们提出了一种方法，通过发展大麦籽粒的高分辨率3-D模型来分析生物标本的形态和内部组成的表型多样性。三维组织学结构是通过从大叠的连续切片材料中重建标本来解决的，这为关键组织在分化中的空间分配提供了基础。通过在多个发育阶段从不同个体中采样和构建模型，我们在计算表象学背景下解决了两个目标：ⅰ）生成平均图集作为功能数据集成的结构参考，以及ⅱ）为以下方面建立数学模型：晶粒形态。我们已经建立了一个算法流水线，用于自动处理大型图像堆栈，以处理表型3-D模型和数据集成，包括配准，多标签分割和功能测量的对齐。所描述的算法允许对包括数千个图像的数据集进行高通量重建和组织识别。该方法的有效性通过自动生成模型得以证明，该模型允许进行组织成分的体积测量，多样性的三维分析，以及通过基于互信息的配准对MALDI-IMS数据进行整合，这对系统分析具有重要意义。分化与发展。

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