Abstract
Today’s volume electron microscopy techniques produce large image datasets on the order of thousands of gigabytes. The vast amount of data makes manual analysis almost infeasible, and data storing and processing challenging. Specialized infrastructure and software was therefore developed during the last decade to address these problems, ranging from distributed and versioned 3D image stores to deep neural network architectures optimized for the segmentation of objects of interest. Illustrated by the example of connectomics, the reconstruction of neural circuitry from 3D images of brain tissue, the most common approaches and solutions are discussed.
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We would like to thank the authors of the software packages listed in Table 1 for providing details on their software.
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Kornfeld, J., Svara, F., Wanner, A.A. (2020). Image Processing for Volume Electron Microscopy. In: Wacker, I., Hummel, E., Burgold, S., Schröder, R. (eds) Volume Microscopy . Neuromethods, vol 155. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0691-9_13
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DOI: https://doi.org/10.1007/978-1-0716-0691-9_13
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