High-precision automated reconstruction of neurons with flood-filling networks
High-precision automated reconstruction of neurons with flood-filling networks
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DOI：
10.1038/s41592-018-0049-4
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发表时间：
2018-08-01
期刊：
影响因子：
48
通讯作者：
Jain, Viren
中科院分区：
文献类型：
--
作者：
Januszewski, Michal;Kornfeld, Joergen;Jain, Viren
从体积电子显微镜数据重建神经回路需要追踪整个细胞，包括所有的神经突。已经开发了自动跟踪方法，但它们的错误率太高，无法在没有大量人工校对的情况下生成可靠的电路图。我们提出了洪水填充网络，这是一种自动分割的方法，类似于大多数以前的努力，使用卷积神经网络，但另外还包含一个循环路径，允许迭代优化和扩展单个神经元过程。我们使用洪水填充网络跟踪神经元在一个斑马雀大脑的连续块面电子显微镜获得的数据集。使用我们的方法，我们实现了平均无误差的神经突路径长度为1.1毫米，我们只观察到四个合并的测试集的路径长度为97毫米。洪水填充网络的性能是一个数量级优于以前的方法应用到这个数据集，虽然大大增加了计算成本。
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.