Automated Reconstruction of Neural Trees Using Front Re-initialization.
Automated Reconstruction of Neural Trees Using Front Re-initialization.
复制标题
使用前端重新初始化自动重建神经树。
DOI：
10.1117/12.912237
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Stepanyants,Armen
中科院分区：
文献类型：
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作者：
Mukherjee,Amit;Stepanyants,Armen
本文提出了一种贪婪算法，用于从光学显微镜堆叠的图像中自动重建神经分支。该算法基于最小费用路法。虽然使用快速行进方法得到的最小代价路径在起点和终点之间产生具有最小累积代价的轨迹，但它不足以用于神经树的重建。这是因为最小成本路径的部分可能错误地通过图像背景而对累积成本造成不可检测的损害。为了绕过这个问题，我们提出了一种算法，它通过迭代重新初始化快速行进前沿来从指定的根生长神经树。快速进行法中使用的速度图像是通过计算梯度矢量场的平均向外通量来生成的。算法的每一次迭代都会产生一个候选扩展，方法是允许前端移动指定的距离，然后从前端的最远点向后跟踪到树。稳健似然比检验用于通过将沿扩展的体素强度与前景和背景中的体素强度进行比较来评估候选扩展的质量。将限定的扩展附加到当前树，重新初始化前端，并继续快速行进，直到满足停止标准。为了评估算法的性能，我们对6层双光子显微图像进行了重建，并将重建结果与地面真实重建的结果进行了比较。平均比较分数为0.82分(满分1.0分)，与专家手动跟踪的表现持平。
This paper proposes a greedy algorithm for automated reconstruction of neural arbors from light microscopy stacks of images. The algorithm is based on the minimum cost path method. While the minimum cost path, obtained using the Fast Marching Method, results in a trace with the least cumulative cost between the start and the end points, it is not sufficient for the reconstruction of neural trees. This is because sections of the minimum cost path can erroneously travel through the image background with undetectable detriment to the cumulative cost. To circumvent this problem we propose an algorithm that grows a neural tree from a specified root by iteratively re-initializing the Fast Marching fronts. The speed image used in the Fast Marching Method is generated by computing the average outward flux of the gradient vector flow field. Each iteration of the algorithm produces a candidate extension by allowing the front to travel a specified distance and then tracking from the farthest point of the front back to the tree. Robust likelihood ratio test is used to evaluate the quality of the candidate extension by comparing voxel intensities along the extension to those in the foreground and the background. The qualified extensions are appended to the current tree, the front is re-initialized, and Fast Marching is continued until the stopping criterion is met. To evaluate the performance of the algorithm we reconstructed 6 stacks of two-photon microscopy images and compared the results to the ground truth reconstructions by using the DIADEM metric. The average comparison score was 0.82 out of 1.0, which is on par with the performance achieved by expert manual tracers.