Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
This work tackles the problem of automating labor-intensive neural circuit reconstruction for neuroscience researchers, but it is incremental as it builds on existing methods for specific tasks.
The paper addresses the challenge of automating neural circuit reconstruction from brain images using convolutional nets, achieving impressive accuracy on clean images but noting that robust handling of image defects remains a major outstanding issue.
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.