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arXiv 1904.129662019-04-29cs.CV

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

MIT（麻省理工学院）；Princeton University（普林斯顿大学）

Kisuk Lee, Nicholas Turner, Thomas Macrina, Jingpeng Wu, Ran Lu, H. Sebastian Seung

英文摘要：

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.

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