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.

从体电子显微镜数据重建神经回路需要追踪整个细胞，包括其所有神经突。已经开发了用于跟踪的自动方法，但是它们的错误率过高而无法生成可靠的电路图，而无需大量的人工校对。我们介绍了洪水填充网络，这是一种自动分段的方法，与大多数以前的工作类似，它使用卷积神经网络，但另外还包含一个循环路径，该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在通过斑马雀科大脑的连续块面电子显微镜获得的数据集中追踪神经元。使用我们的方法，我们获得了1.1 mm的平均无误差神经突路径长度，并且我们在路径长度为97 mm的测试集中仅观察到四次合并。尽管计算量大大增加，但洪水填充网络的性能比应用于该数据集的先前方法好一个数量级。