Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
Bioinformatics. 2010 Apr 15;26(8):1091-7. doi: 10.1093/bioinformatics/btq072. Epub 2010 Feb 19.
The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.
We proposed to standardize a larval image by best aligning its principal skeleton (PS), and thus used this method as an alternative of the usually considered affine alignment. The PS of a shape was defined as a series of connected polylines that spans the entire shape as broadly as possible, but with the shortest overall length. We developed an automatic PS detection algorithm to robustly detect the PS from an image. Then for a pair of larval images, we designed an automatic image registration method to align their PSs and the entire images simultaneously. Our experimental results on both simulated images and real datasets showed that our method does not only produce satisfactory results for real confocal larval images, but also perform robustly and consistently when there is a lot of noise in the data. We also applied this method successfully to confocal images of some other patterns such as the adult fruit fly VNC and center brain, which have more complicated PS. This demonstrates the flexibility and extensibility of our method.
The supplementary movies, full size figures, test data, software, and tutorial on the software can be downloaded freely from our website http://penglab.janelia.org/proj/principal_skeleton.
果蝇（Drosophila melanogaster）是生物学中常用的模式生物。我们目前正在基于大量果蝇幼虫 GAL4 系的集合构建果蝇幼虫神经系统（LNS）的三维数字图谱，每个 GAL4 系都靶向神经元的一个子集。为了实现这一目标，我们需要自动对齐这些 GAL4 系的许多高分辨率共聚焦图像堆栈。在图像模式配准中常用的策略之一是首先使用仿射变换全局对齐图像，然后进行局部非线性变形。不幸的是，LNS 的空间结构复杂，经常扭曲，使得直接使用仿射方法难以直接全局对齐图像。在构建成年果蝇腹神经索（VNC）三维数字图谱的并行项目中，我们也面临着类似的问题。
我们建议通过最佳对齐幼虫的主骨架（PS）来标准化图像，并将其作为通常考虑的仿射对齐的替代方法。形状的 PS 定义为一系列连接的折线，尽可能广泛地跨越整个形状，但总体长度最短。我们开发了一种自动 PS 检测算法，可从图像中稳健地检测 PS。然后，对于一对幼虫图像，我们设计了一种自动图像配准方法来同时对齐它们的 PS 和整个图像。我们在模拟图像和真实数据集上的实验结果表明，我们的方法不仅可以为真实的共聚焦幼虫图像产生令人满意的结果，而且在数据存在大量噪声时也能稳健且一致地运行。我们还成功地将此方法应用于其他一些模式（如成年果蝇 VNC 和中央脑）的共聚焦图像，这些模式的 PS 更为复杂。这证明了我们方法的灵活性和可扩展性。
补充电影、全尺寸图、测试数据、软件以及有关该软件的教程可从我们的网站 http://penglab.janelia.org/proj/principal_skeleton 上免费下载。
