flâneur

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

ncbi.nlm.nih.gov · 8,340 words · saved by 1 readers

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now. 1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA 2Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA 2Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA 3Neuroscience Institute, Princeton University, Princeton, NJ, 08544, USA 3Neuroscience Institute, Princeton University, Princeton, NJ, 08544, USA 2Department of Computer Science, Princeton University, Princeton, NJ, 08544,

. Author manuscript; available in PMC: 2020 May 6. Published in final edited form as: Curr Opin Neurobiol. 2019 May 6;55:188–198. doi: 10.1016/j.conb.2019.04.001 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…

saved by

related reading