Active learning of neuron morphology for accurate automated tracing of neurites.
Active learning of neuron morphology for accurate automated tracing of neurites.
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DOI：
10.3389/fnana.2014.00037
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发表时间：
2014
影响因子：
2.9
通讯作者：
Stepanyants A
中科院分区：
文献类型：
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作者：
Gala R;Chapeton J;Jitesh J;Bhavsar C;Stepanyants A
从光学显微镜图像堆中自动追踪神经突的过程对于大规模或高通量的神经回路定量研究至关重要。虽然标记的神经突的一般布局可以被许多自动跟踪算法捕获，但通常不可能可靠地区分属于不同细胞的过程。原因是由于标记不完善，堆栈中的一些神经突可能出现断裂，而由于光学显微镜的分辨率有限，其他神经突可能出现融合。训练有素的神经解剖学家通常通过结合分支之间的距离、分支方向、强度、口径、弯曲度、颜色以及棘或扣的存在等信息，在手动跟踪任务中解决这种拓扑模糊性。同样，为了自动评估不同的拓扑场景，我们开发了一种结合了上述许多特征的机器学习方法。在用户辅助跟踪过程中，使用专门设计的置信度度量来主动训练算法。主动学习显著减少了训练时间，并且通过提供很少的训练样例，可以获得小于1%的泛化错误率。为了评估算法的整体性能，许多图像堆栈被自动重建，以及由几个训练有素的用户手动重建，使得将自动跟踪与基线用户间可变性进行比较成为可能。选择轨迹的几个几何和拓扑特征进行比较。这些特征包括总走线长度、支路和端点的总数、相应走线的亲和性以及相应支路和端点之间的距离。我们的研究结果表明，当标记的神经突密度足够低时，自动化的痕迹与训练有素的用户获得的人工重建没有显著差异。
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.
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影响因子：
2.5
作者：
Losavio, Bradley E.;Liang, Yong;Saggau, Peter
通讯作者：
Saggau, Peter
影响因子：
3
作者：
Gillette, Todd A.;Brown, Kerry M.;Ascoli, Giorgio A.
通讯作者：
Ascoli, Giorgio A.
DOI：
10.1073/pnas.1211467109
发表时间：
2012-12-18
影响因子：
11.1
作者：
Chapeton, Julio;Fares, Tarec;Stepanyants, Armen
通讯作者：
Stepanyants, Armen
影响因子：
3
作者：
Cannon, RC;Turner, DA;Wheal, HV
通讯作者：
Wheal, HV
影响因子：
64.8
作者：
通讯作者：
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