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Automated reconstruction of neurites from 3D microscopy image stacks

从 3D 显微镜图像堆栈自动重建神经突

基本信息

批准号：
8236970
负责人：
ARMEN STEPANYANTS
金额：
$ 26.75万
依托单位：
NORTHEASTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2014-03-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Currently, accurate methods of analysis of neuron morphology are based on manual or semi-automated tracing systems. Such tracings can be time consuming and/or are prone to errors in situations where faint or beaded neurites diffusely cover large volumes. This is usually the case with tracing axons of cortical pyramidal neurons, e.g. long range horizontal projections. With this proposal we aim to develop a tool which will automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing neural circuits research. As axons of many neuron types can span the entire brain of an animal (e.g. cortical pyramidal cell axons) or the entire animal itself (e.g. C. elegans), our ultimate goal is to perform reconstructions on a large scale to recover axonal and dendritic arbors of sparsely labeled populations of neurons in their entirety. Our algorithm consists of two main parts. First, a 3D stack of images is segmented into regions based on a local watershed type segmentation procedure. For this, preferred orientations are calculated in each voxel of the thresholded stack of images by applying a bank of steerable 3D Gabor filters. Regions are grown by stepping down in intensity and placing edges between adjacent voxels with dissimilar orientations. Second, created regions are merged into larger structures using global optimization criteria. Here, optimal connecting paths are determined for every pair of regions by maximizing the intensity along the path and, at the same time, keeping the path length to a minimum. Regions are merged depending on the intensity and curvature along their optimally connecting paths. The specific aims of this proposal are as follows. Specific Aim 1: We will develop a graphic user interface (GUI) and optimization based algorithm for the semi-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will be based on the gradient ascent method for finding optimal paths which connect user specified seed points. The GUI will provide the user fast and flexible control over the details of the procedure. We will develop this semi-automated reconstruction tool to function as an autonomous unit, but the GUI and the tracing algorithm are also essential parts of the Specific Aim 2. Specific Aim 2: We will develop a segmentation based algorithm for a fully-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will use watershed type segmentation combined with global optimization based criteria for merging the segmented regions. The fully-automated algorithm will be implemented in the GUI and will utilize methods developed as part of the Specific Aim 1. Specific Aim 3: We will complete the reconstruction process by automatically detecting neuron cell bodies, branching structure, axonal boutons, and dendritic spines. The GUI will provide an opportunity to correct possible errors by connecting and disconnecting branches, removing and adding branches, spines, and boutons. Simple morphometric functions, such as the calculation of length and numbers of boutons and spines, will be implemented as well. Currently, accurate methods of quantitative analysis of neuron morphology and synaptic connectivity are based on manual or semi-automated tracing tools which are time consuming and can be prone to errors. With this proposal we aim to develop a tool that will fully-automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing basic neural circuits research and understanding changes in the central nervous system which underlie its disease state.

描述(申请人提供)：目前，神经元形态的准确分析方法是基于手动或半自动跟踪系统。在模糊或珠状突起弥漫覆盖大体积的情况下，这种示踪可能很耗时和/或容易出错。这通常是跟踪皮质锥体神经元轴突的情况，例如远程水平投射。有了这项建议，我们的目标是开发一种工具，可以从3D显微镜图像堆叠中自动重建神经突起的过程。这种工具的存在对于推进神经电路研究是至关重要的。由于许多神经元类型的轴突可以横跨动物的整个大脑(例如皮质锥体细胞轴突)或整个动物本身(例如线虫)，我们的最终目标是进行大规模的重建，以完整地恢复稀疏标记的神经元群体的轴突和树突。我们的算法由两个主要部分组成。首先，基于局部分水岭类型的分割过程将3D图像堆叠分割成区域。为此，通过应用一组可引导的3D Gabor过滤器来计算阈值图像堆栈的每个体素中的优选方向。通过降低强度并在具有不同方向的相邻体素之间放置边缘来增长区域。其次，使用全局优化准则将创建的区域合并到更大的结构中。这里，通过最大化沿路径的强度并同时将路径长度保持为最小，为每一对区域确定最佳连接路径。区域将根据其最佳连接路径上的强度和曲率进行合并。这项建议的具体目标如下。具体目标1：我们将开发一个图形用户界面(GUI)和基于优化的算法，用于从3D显微镜图像堆叠中半自动跟踪神经突起。该算法将基于梯度上升法寻找连接用户指定种子点的最优路径。图形用户界面将为用户提供对过程细节的快速而灵活的控制。我们将开发这个半自动重建工具作为一个独立的单元，但图形用户界面和跟踪算法也是特定目标2的基本部分。特定目标2：我们将开发一个基于分割的算法，用于从3D显微镜图像堆栈中全自动跟踪轴突。该算法将使用分水岭类型的分割结合基于全局优化的准则来合并分割后的区域。全自动算法将在图形用户界面中实现，并将利用作为特定目标1的一部分开发的方法。特定目标3：我们将通过自动检测神经元胞体、分支结构、轴突和树突来完成重建过程。通过连接和断开分支、删除和添加分支、脊椎和圆环，图形用户界面将提供纠正可能的错误的机会。还将实现简单的形态测量功能，如计算Bouton和Spine的长度和数量。目前，准确的神经元形态和突触连接的定量分析方法是基于人工或半自动的跟踪工具，这些工具既耗时又容易出错。有了这项建议，我们的目标是开发一种工具，从3D显微镜图像堆叠中完全自动化重建神经突起的过程。这种工具的存在对于推进基本神经回路研究和了解作为其疾病状态基础的中枢神经系统的变化至关重要。