Fully-Automatic Synapse Prediction and Validation on a Large Data Set.
Fully-Automatic Synapse Prediction and Validation on a Large Data Set.
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DOI：
10.3389/fncir.2018.00087
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发表时间：
2018
影响因子：
3.5
通讯作者：
Plaza SM
中科院分区：
文献类型：
--
作者：
Huang GB;Scheffer LK;Plaza SM
从电子显微镜（EM）数据集中提取连接体需要识别神经元并确定神经元之间的连接（突触）。由于手动提取这些信息非常耗时，因此已经进行了大量的研究工作来自动分割神经元，以帮助指导并最终取代手动跟踪。直到最近，对神经元之间实际突触的自动检测的研究相对较少。这种差异在一定程度上可以归因于几个因素：获得神经元形状是提取连接组第一步的先决条件，手动追踪比注释突触耗时得多，并且神经元接触面积可以用作突触的代理在确定连接时。然而，最近的研究表明，接触面积本身并不足以预测突触连接。此外，随着分割的改进，我们观察到突触注释消耗了整体重建时间的更大部分（占总工作量的50%以上）。随着分割的改善，这个比率只会变得更糟，从而限制了整体可能的加速。因此，我们通过开发自动检测突触前神经元及其突触后伙伴的算法来解决这个问题。特别是，使用U-Net卷积神经网络（CNN）检测突触前结构，使用具有以局部分割为条件的特征的多层感知器（MLP）检测突触后伙伴。这项工作是新颖的，因为它需要最少的训练量，直接利用图像分割的进步，并为多元突触检测提供了一个完整的解决方案。我们进一步引入新的指标来评估我们的算法有意义的大小的连接体。当应用于我们的方法对Drosphila的EM数据的输出时，这些指标表明，完全自动的预测可以用于有效地正确表征大多数连接性。
Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of connections (synapses) between neurons. As manual extraction of this information is very time-consuming, there has been extensive research efforts to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively little research on automatic detection of the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite for the first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections. However, recent research has demonstrated that contact area alone is not a sufficient predictor of a synaptic connection. Moreover, as segmentation improved, we observed that synapse annotation consumes a more significant fraction of overall reconstruction time (upwards of 50% of total effort). This ratio will only get worse as segmentation improves, gating the overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect presynaptic neurons and their postsynaptic partners. In particular, presynaptic structures are detected using a U-Net convolutional neural network (CNN), and postsynaptic partners are detected using a multilayer perceptron (MLP) with features conditioned on the local segmentation. This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. When applied to the output of our method on EM data from Drosphila, these metrics demonstrate that a completely automatic prediction can be used to effectively characterize most of the connectivity correctly.
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影响因子：
3.7
作者：
Kreshuk A;Koethe U;Pax E;Bock DD;Hamprecht FA
通讯作者：
Hamprecht FA
影响因子：
5.1
作者：
Jagadeesh, Vignesh;Anderson, James;Manjunath, B. S.
通讯作者：
Manjunath, B. S.
影响因子：
7.7
作者：
Staffler, Benedikt;Berning, Manuel;Helmstaedter, Moritz
通讯作者：
Helmstaedter, Moritz
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
48
作者：
Boergens, Kevin M.;Berning, Manuel;Helmstaedter, Moritz
通讯作者：
Helmstaedter, Moritz