Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA.
Front Neuroanat. 2014 May 19;8:37. doi: 10.3389/fnana.2014.00037. eCollection 2014.
Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.
从显微镜图像堆栈中自动追踪神经突对于大规模或高通量的神经回路定量研究至关重要。虽然许多自动化追踪算法可以捕捉到标记神经突的总体布局，但通常无法可靠地区分属于不同细胞的过程。原因是堆栈中的一些神经突可能由于标记不完美而显得断裂，而另一些神经突可能由于光学显微镜的分辨率有限而显得融合。受过训练的神经解剖学家在手动追踪任务中通过结合分支之间的距离、分支方向、强度、口径、曲折度、颜色以及棘突或boutons 的存在等信息，经常解决这些拓扑上的歧义。同样，为了自动评估不同的拓扑场景，我们开发了一种机器学习方法，该方法结合了上述许多特征。专门设计的置信度度量用于在用户辅助追踪过程中主动训练算法。主动学习通过提供少量训练示例显著减少了训练时间，并使获得小于 1%的泛化误差率成为可能。为了评估算法的整体性能，我们自动重建了一些图像堆栈，并由几位经过训练的用户手动重建，从而可以将自动追踪与基线用户间变异性进行比较。为了进行比较，选择了一些轨迹的几何和拓扑特征。这些特征包括总轨迹长度、分支和末端点数、对应轨迹的亲和度以及对应分支和末端点之间的距离。我们的结果表明，当标记的神经突密度足够低时，自动追踪与经过训练的用户获得的手动重建没有显著差异。
