Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
BMC Bioinformatics. 2013 Mar 12;14:93. doi: 10.1186/1471-2105-14-93.
Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.
We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based "force" fields are used: one for "pressure", which is the distance transform field of foreground pixels (voxels) to the background, and another for "thrust", which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can "push" a "rolling ball" quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.
We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.
在存在噪声的微观图像中自动对嵌入的神经元进行 3D 数字重建（追踪）是一项具有挑战性的任务，尤其是当细胞形态复杂时。
我们开发了一种名为 DF-Tracing 的新方法来应对这一挑战。该方法首先通过各向异性滤波和自动阈值处理从噪声图像中提取神经突信号（前景）。然后，DF-Tracing 在提取的前景神经突信号上执行耦合距离场（DF）算法，并自动重建神经元形态。使用了两个基于距离变换的“力”场：一个用于“压力”，即前景像素（体素）到背景的距离变换场，另一个用于“推力”，即前景像素到自动确定的种子点的距离变换场。这两个力场的耦合可以“推动”一个“滚动球”沿着神经元的骨架快速滚动，从而重建 3D 细胞形态。
我们使用 DF-Tracing 对使用 3D 激光显微镜获得的、存在噪声的蜻蜓胸部神经节图像堆栈中的复杂神经元结构进行了重建。与几种先前的方法相比，DF-Tracing 产生了更好的重建效果。
