PDF(Pubmed)
Abstract：
The digital extraction of detailed neuronal morphologies from microscopy data is an essential step in the study of neurons. Ever since Cajal\'s work, the acquisition and analysis of neuron anatomy has yielded invaluable insight into the nervous system, which has led to our present understanding of many structural and functional aspects of the brain and the nervous system, well beyond the anatomical perspective. Obtaining detailed anatomical data, though, is not a simple task. Despite recent progress, acquiring neuron details still involves using labor-intensive, error prone methods that facilitate the introduction of inaccuracies and mistakes. In consequence, getting reliable morphological tracings usually needs the completion of post-processing steps that require user intervention to ensure the extracted data accuracy. Within this framework, this paper presents NeuroEditor, a new software tool for visualization, editing and correction of previously reconstructed neuronal tracings. This tool has been developed specifically for alleviating the burden associated with the acquisition of detailed morphologies. NeuroEditor offers a set of algorithms that can automatically detect the presence of potential errors in tracings. The tool facilitates users to explore an error with a simple mouse click so that it can be corrected manually or, where applicable, automatically. In some cases, this tool can also propose a set of actions to automatically correct a particular type of error. Additionally, this tool allows users to visualize and compare the original and modified tracings, also providing a 3D mesh that approximates the neuronal membrane. The approximation of this mesh is computed and recomputed on-the-fly, reflecting any instantaneous changes during the tracing process. Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. The edited morphology can then be stored, together with the corresponding 3D mesh that approximates the neuronal membrane.
摘要：
从显微镜数据中数字提取详细的神经元形态是研究神经元的重要步骤。自从Cajal工作以来,对神经元解剖结构的获取和分析产生了对神经系统的宝贵见解，这导致了我们目前对大脑和神经系统的许多结构和功能方面的理解，远远超出了解剖学的角度。获得详细的解剖数据，虽然,不是一个简单的任务。尽管最近取得了进展，获取神经元细节仍然涉及使用劳动密集型，容易出错的方法，便于引入不准确和错误。因此，获得可靠的形态学描迹通常需要完成后处理步骤，这些步骤需要用户干预以确保提取的数据准确性。在这个框架内,本文介绍了NeuroEditor，一种新的可视化软件工具，编辑和纠正先前重建的神经元轨迹。该工具是专门为减轻与获取详细形态相关的负担而开发的。NeuroEditor提供了一组算法，可以自动检测跟踪中潜在错误的存在。该工具便于用户通过简单的鼠标单击来探索错误，以便可以手动更正错误，如果适用，自动。在某些情况下,该工具还可以提出一组操作来自动纠正特定类型的错误。此外,该工具允许用户可视化和比较原始和修改后的轨迹，还提供近似神经元膜的3D网格。该网格的近似值是在运行中计算和重新计算的，反映跟踪过程中的任何瞬时变化。此外，NeuroEditor可以由用户轻松扩展，谁可以用Python编写自己的算法并在工具中运行它们。最后,本文包含一个示例，展示了用户如何通过应用一系列编辑操作来轻松定义自定义工作流。然后可以存储编辑的形态，以及近似神经元膜的相应3D网格。
