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- W2950896314 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." @default.
- W2950896314 created "2019-06-27" @default.
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- W2950896314 date "2019-04-29" @default.
- W2950896314 modified "2023-09-27" @default.
- W2950896314 title "Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy" @default.
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