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Sara Patterson edited this page Sep 6, 2018 · 5 revisions

Welcome to the SBFSEM-tools wiki! Please note: this is under construction and will soon move to the official Neitz lab repository.

Getting started

Scroll down to read more about the program, or check out some pictures of SBFSEM-tools' capabilities.

If you want to use SBFSEM-tools to write your own code, read about the Neuron class, then check out tools for working with Viking's API.

If you want to avoid programming entirely, check out the user interface. If you're new to Matlab, there's information about setup in the old documentation.

About

SBFSEM-tools is a open source MATLAB toolbox developed in the Neitz Lab at the University of Washington. SBFSEM-tools was built around the Viking annotation software, however, many aspects are quite general and may apply easily to other programs and imaging methods.

SBFSEM-tools provides Matlab support for accessing the connectome annotation database API. Annotation data is queried through Viking's OData service and parsed into Matlab data types. This is abstracted so users can work with familiar objects like neurons, synapses, etc. SBFSEM-tools provides a framework to support data mining and user-defined analysis. However, the key functionality can be accessed without programming through user interfaces.

Importantly, this program is designed around an interest in open sourcing the data and code used in scientific research. Resources to enable sharing the data and code used in by this program for publications are in place. Check back for examples as we will post the data and code used in our publications to the NeitzLab Github.

Key features:

  • Efficent, accurate 3D rendering of neurons:
    • Volume rendering of Closed Curve annotations
    • Polygon meshes from Disc annotations
    • Segmentation and volume rendering of free-form traces over a stack of EM images.
  • Standard analysis routines for both single neurons and networks.
  • Image registration: surfaces from IPL boundary markers, XY offset calculations.
  • Support for generating high resolution, publication quality images
  • Export 3D models to use in programs like Blender, NEURON and ParaView.

Work in progress:

  • A more intuitive representation of connectivity networks. For now, this program is best for analyses and visualization of single neurons. See the tools for network analysis developed by Jamie Anderson in the Jones lab: py-connectome-analysis, TulipPaths.
  • A more generalized framework for OData queries (in the meantime, contact me if you would like a [Postman][postman] collection explaining common queries).

For more information:

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