Dsilva Carmeline J, Lim Bomyi, Lu Hang, Singer Amit, Kevrekidis Ioannis G, Shvartsman Stanislav Y
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA These authors contributed equally to this work.
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Development. 2015 May 1;142(9):1717-24. doi: 10.1242/dev.119396. Epub 2015 Apr 1.
Progress of development is commonly reconstructed from imaging snapshots of chemical or mechanical processes in fixed tissues. As a first step in these reconstructions, snapshots must be spatially registered and ordered in time. Currently, image registration and ordering are often done manually, requiring a significant amount of expertise with a specific system. However, as the sizes of imaging data sets grow, these tasks become increasingly difficult, especially when the images are noisy and the developmental changes being examined are subtle. To address these challenges, we present an automated approach to simultaneously register and temporally order imaging data sets. The approach is based on vector diffusion maps, a manifold learning technique that does not require a priori knowledge of image features or a parametric model of the developmental dynamics. We illustrate this approach by registering and ordering data from imaging studies of pattern formation and morphogenesis in three model systems. We also provide software to aid in the application of our methodology to other experimental data sets.
发育过程通常是根据固定组织中化学或机械过程的成像快照来重建的。在这些重建的第一步中，快照必须在空间上对齐并按时间排序。目前，图像对齐和排序通常是手动完成的，这需要对特定系统有大量专业知识。然而，随着成像数据集规模的增长，这些任务变得越来越困难，尤其是当图像有噪声且所研究的发育变化很细微时。为应对这些挑战，我们提出了一种自动方法，可同时对齐成像数据集并按时间排序。该方法基于向量扩散映射，这是一种流形学习技术，不需要图像特征的先验知识或发育动力学的参数模型。我们通过对齐和排序来自三个模型系统中模式形成和形态发生成像研究的数据来说明这种方法。我们还提供了软件，以帮助将我们的方法应用于其他实验数据集。
