Bioinformatics. 2017 Apr 1;33(7):1073-1080. doi: 10.1093/bioinformatics/btw751.
The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.
Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art.
Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd .
Supplementary data are available at Bioinformatics online.
神经元的功能及其在神经元网络中的作用与细胞形态紧密相关。许多旨在揭示这种联系的神经生物学研究中的一个基本问题是从微观图像数据中对神经元细胞形态进行数字重建。为此已经开发了许多方法，但它们远非完美，需要更好的方法。
这里我们提出了一种新的用于追踪神经元中心轴的方法，这是完整重建所必需的。该方法使用与以前的方法完全不同的方法，将神经元追踪视为贝叶斯多目标跟踪问题。该问题使用概率假设密度滤波来解决。对真实神经元的 2D 和 3D 荧光显微镜图像数据集进行实验的结果表明，所提出的方法的性能可与最先进的方法相媲美，甚至更好。
实现所提出的神经元追踪方法的软件是用 Java 编程语言编写的，作为 ImageJ 平台的一个插件。源代码可在非商业用途的情况下在 https://bitbucket.org/miroslavradojevic/phd 上免费获得。
补充数据可在“Bioinformatics”在线获取。
