SynEM, automated synapse detection for connectomics
SynEM, automated synapse detection for connectomics
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DOI：
10.7554/elife.26414
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发表时间：
2017-07-14
期刊：
影响因子：
7.7
通讯作者：
Helmstaedter, Moritz
中科院分区：
文献类型：
--
作者：
Staffler, Benedikt;Berning, Manuel;Helmstaedter, Moritz
神经组织中含有高密度的化学突触，在哺乳动物的大脑皮层中大约每m3就有1个。因此，即使是小块的神经组织，密集的连接映射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，但当数据集的大小增加并且需要密集映射时，突触检测变得有限。在这里，我们报告SynEM，一种从常规整块染色的3D电子显微镜图像堆栈中自动检测突触的方法。该方法是基于图像数据的分割，并侧重于神经元的突触或非突触过程之间的分类边界。SynEM在没有用户交互的情况下在二进制皮层连接组中产生97%的精确度和召回率。它可以扩展到大量的皮质神经元数据，甚至是全脑数据集。SynEM为大型密集映射的连接体消除了手动突触注释的负担。
Nerve tissue contains a high density of chemical synapses, about 1 per mu m(3) 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.