Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
Biomimetic Robotics and Machine Learning, Munich, Germany.
Elife. 2017 Jul 14;6:e26414. doi: 10.7554/eLife.26414.
Nerve tissue contains a high density of chemical synapses, about 1 per µm in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.
神经组织包含高密度的化学突触，在哺乳动物大脑皮层中，每平方微米约有 1 个突触。因此，即使对于小块的神经组织，密集的连接组学图谱绘制也需要识别数百万到数十亿个突触。虽然连接组学数据分析的重点一直是神经突重建，但当数据集规模增大且需要密集映射时，突触检测就会成为限制因素。在这里，我们报告了 SynEM，这是一种从传统的整体染色 3D 电子显微镜图像堆栈中自动检测突触的方法。该方法基于图像数据的分割，并专注于将神经元过程之间的边界分类为突触或非突触。SynEM 在没有用户交互的情况下，在二进制皮质连接组中实现了 97%的精度和召回率。它可以扩展到大量皮质神经胶质，甚至整个大脑数据集。SynEM 为大规模密集映射的连接组学图谱绘制减轻了手动突触注释的负担。
