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:description: Official documentation for vesicle: Volumetric Evaluation of Synaptic Inferfaces using Computer vision at Large Scale
:keywords: synapse, connectomics, object detection, computer vision, pipeline
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vesicle
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vesicle: Volumetric Evaluation of Synaptic Inferfaces using Computer vision at Large Scale
vesicle provides a context-aware method for scalable synapse detection in anisotropic electron microscopy data. We provide two methods for object detection: vesicle-rf and vesicle-cnn, which have computational and performance tradeoffs.
This work also resulted in the creation of a general-purpose object detection framework that can be used in a LONI pipelining environment. We explain this detection paradigm and provide vesicle-rf code as an example.
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.. sidebar:: vesicle Contact Us
If you have questions about vesicle, or have data to analyze, let us know: support@neurodata.io
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:maxdepth: 1
:caption: Documentation
sphinx/introduction
sphinx/local_config
sphinx/neurodata
sphinx/faq
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:maxdepth: 1
:caption: Tutorials
tutorials/vesiclerf
tutorials/vesiclecnn
.. toctree::
:maxdepth: 1
:caption: Paper
paper/bmvc
paper/results
.. toctree::
:maxdepth: 1
:caption: Further Reading
api/functions
api/loni
Gitter chatroom
Mailing List
Github repo
Release Notes
If you use vesicle or its data derivatives, please cite:
William Gray Roncal, Michael Pekala, Verena Kaynig-Fittkau, Dean M Kleissas, Joshua T Vogelstein, Hanspeter Pfister, Randal Burns, R Jacob Vogelstein, Mark A Chevillet and Gregory D Hager. VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer Vision at Large Scale. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 81.1-81.13. BMVA Press, September 2015.