<commit_before># NeuroProof 1.1 [![Picture](https://raw.github.com/janelia-flyem/janelia-flyem.github.com/master/images/HHMI_Janelia_Color_Alternate_180x40.png)](http://www.janelia.org)
##Toolkit for Graph-based Image Segmentation and Analysis

The NeuroProof software is an image segmentation tool currently being used
in [the FlyEM project at Janelia Farm Research Campus](http://janelia.org/team-project/fly-em)
to help reconstruct neuronal structure in the fly brain.  This tool
provides routines for efficiently agglomerating an initial volume that
is over-segmented.  It provides several advances over Fly EM's previous, but actively maintained tool, [Gala](https://github.com/janelia-flyem/gala):

* Faster implementation of agglomeration (written in C++ instead of Python)
* Incorporation of biological priors (like mitochondria) to improve the quality of the segmentation
(see [Parag, et al '15](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125825))

* Interactive training
* Introduction of new metrics for analyzing segmentation quality

While NeuroProof has been tested in the domain
of EM reconstruction, we believe it to be widely applicable to other
application domains.  In addition to graph agglomeration tools, this
package also provides routines for estimating the uncertainty of a segmentation
[Plaza, et al '12](http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0044448),
algorithms to compare segmentation with ground truth, and focused proofreading algorithms
to accelerate semi-manual EM tracing [Plaza '14](http://arxiv.org/abs/1409.1199).<commit_msg>Adding link to MICCAI14 paper<commit_after># NeuroProof 1.1 [![Picture](https://raw.github.com/janelia-flyem/janelia-flyem.github.com/master/images/HHMI_Janelia_Color_Alternate_180x40.png)](http://www.janelia.org)
##Toolkit for Graph-based Image Segmentation and Analysis

The NeuroProof software is an image segmentation tool currently being used
in [the FlyEM project at Janelia Farm Research Campus](http://janelia.org/team-project/fly-em)
to help reconstruct neuronal structure in the fly brain.  This tool
provides routines for efficiently agglomerating an initial volume that
is over-segmented.  It provides several advances over Fly EM's previous, but actively maintained tool, [Gala](https://github.com/janelia-flyem/gala):

* Faster implementation of agglomeration (written in C++ instead of Python)
* Incorporation of biological priors (like mitochondria) to improve the quality of the segmentation
(see [Parag, et al '15](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125825))
* Interactive training of superpixel boundaries without exhaustive groundtruth ([Parag, et.al. 14] (http://www.researchgate.net/publication/265683774_Small_Sample_Learning_of_Superpixel_Classifiers_for_EM_Segmentation))
* Introduction of new metrics for analyzing segmentation quality

While NeuroProof has been tested in the domain
of EM reconstruction, we believe it to be widely applicable to other
application domains.  In addition to graph agglomeration tools, this
package also provides routines for estimating the uncertainty of a segmentation
[Plaza, et al '12](http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0044448),
algorithms to compare segmentation with ground truth, and focused proofreading algorithms
to accelerate semi-manual EM tracing [Plaza '14](http://arxiv.org/abs/1409.1199).
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