Abstract
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.
Abstract (translated)
神经回路可以通过连续切片电子显微镜获取的脑图像重建。半个世纪以来，图像分析一直是由人工完成的，而自动化的工作几乎可以追溯到现在。卷积网在十几年前首次应用于神经元边界检测，现在已经在干净的图像上取得了令人印象深刻的准确性。图像缺陷的鲁棒处理是一个重大的突出挑战。卷积网络也被用于神经回路重建中的其他任务：寻找突触和识别突触伙伴，扩展或修剪神经元重建，以及对齐序列图像以创建三维图像堆栈。计算系统正被设计用来处理立方毫米脑体积的petavoxel图像。
URL
https://arxiv.org/abs/1904.12966
