##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 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:
- 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)
- 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, algorithms to compare segmentation with ground truth, and focused proofreading algorithms to accelerate semi-manual EM tracing Plaza '14.
###Features
- Algorithm for efficiently learning an agglomeration classifier
- Algorithm for efficiently agglomerating an oversegmented volume
- Implementation of variation of information metric for comparing two labeled volumes
- Algorithm to estimate uncertainty in the segmentation graph
- Tools to assess the amount of work required to edit/revise a segmentation
- GUI front-end for visualizing segmentation results; interface for merging segments together
- Algorithm for performing focused training (active learning based supervoxel merging classification) using simple GUI
- Simple and access-efficient graph-library implementation and straightforward conversion to powerful boost graph library
- Data stack implementation that allows one to leverage image processing algorithms in Vigra
- Python bindings to enable accessing the Rag, segmentation routines, and editing operations in various tool environments like in Gala and Raveler
NeuroProof has been tested on several different linux environments and compiles on Mac. Documentation in NeuroProof follow Doxygen comment conventions; an html view can be created by running the following command:
% doxygen doxygenconfig.file
Neuroproof has several dependencies. In principle, all of these dependencies can be built by hand and then the following commands issued:
% mkdir build; cd build
% cmake ..
% make; make install
To simplify the build we now use the conda-build tool. The resulting binary is uploaded to the flyem binstar channel, and can be installed using the conda package manager. The installation will install all of the neuroproof binaries (including the interactive tool) and the python libraries.
The NeuroProof dependencies can be found in Fly EM's conda recipes.
buildem is no longer supported. Instructions on how to compile/develop NeuroProof with minimal interaction with conda are provided below.
The Miniconda tool first needs to installed:
# Install miniconda to the prefix of your choice, e.g. /my/miniconda
# LINUX:
wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
bash Miniconda-latest-Linux-x86_64.sh
# MAC:
wget https://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh
bash Miniconda-latest-MacOSX-x86_64.sh
# Activate conda
CONDA_ROOT=`conda info --root`
source ${CONDA_ROOT}/bin/activate root
Once conda is in your system path, call the following to install neuroproof:
% conda create -n CHOOSE_ENV_NAME -c flyem neuroproof
Conda allows builder to create multiple environments. To use the executables
and libraries, set your PATH to the location of PREFIX/CHOOSE_ENV_NAME/bin.
Note: This should work on many distributions of linux and on Mac OSX 10.10+. For Mac,
you will need to set DYLD_FALLBACK_LIBRARY_PATH to PREFIX/CHOOSE_ENV_NAME/lib.
Note: For now, the NeuroProof conda package expects certain files to live in /usr/lib64/.
On Ubuntu, you may need to run sudo ln -s /usr/lib/x86_64-linux-gnu /usr/lib64 to use NeuroProof.
(This is currently tracked as issue #6.)
Developing has never been easier using conda. If you plan to actively modify
the code, first install neuroproof as discussed above. Then clone
this repository into the directory of your choosing. The package cmake
can still be used but the environment variables must be set to point to
the dependencies and libraries stored in PREFIX/CHOOSE_ENV_NAME. NeuroProof
includes a simple wrapper script configure-for-conda.sh that simply
calls the build script from NeuroProof's recipe with the appropriate environment variables.
% ./configure-for-conda.sh /path/to/your/environment-prefix
That will create a directory named build (if necessary) configured to install to your environment prefix.
(If necessary, it will install gcc first.) To actually run the build, try these commands:
% cd build
% make
% make install
To use (or test) your custom build of NeuroProof, you'll need to set the LD_LIBRARY_PATH and PYTHONPATH environment variables:
% export LD_LIBRARY_PATH=/path/to/your/environment-prefix/lib
% export PYTHONPATH=/path/to/NeuroProof/build/python
% make test
For coding that requires adding new dependencies please consult documentation for building conda builds and consult Fly EM's conda recipes.
Contributors should verify regressions using 'make test' and submit pull requests so that the authors can properly update the binstar repository and build system.
So, the full build process, from scratch, is this:
# Set up a conda environment with all dependencies
conda create -n myenv -c flyem neuroproof
source activate myenv
PREFIX=$(conda info --root)/envs/myenv
export LD_LIBRARY_PATH=${PREFIX}/lib # Linux
export DYLD_FALLBACK_LIBRARY_PATH=${PREFIX}/lib # Mac
# Discard the neuroproof downloaded binary; we'll build our own.
conda remove neuroproof
# Clone and build
git clone https://github.com/janelia-flyem/neuroproof
cd neuroproof
./configure-for-conda.sh ${PREFIX}
cd build
make -j4
make install
make testIf one generates edge probability between segments, focused proofreading can be used. The original focused proofreading approach is implemented in NeuroProof and available as a plugin within Raveler.
Edge probability can be determined using the agglomeration executable neuroproof_graph_predict. After agglomerating the segmentation, a final graph is produced which can be used for focused proofreading. (Other techniques to generate uncertainty will be compatible with neuroproof as long as the format matches the graph output of agglomeration).
Install Raveler and launch the focused proofreading workflow providing a body importance threshold and a graph json file produced by neuroproof_graph_predict. In the current workflow, Raveler will look for all paths connecting bodies whose risk (impact * uncertainty) is high enough to warrant examination. For a given important path, it will navigate to the first edge in the path, and recompute uncertainty for each merge/no merge decision. Merge decisions currently do not result in re-running the edge classifier, rather the highest affinity probability is used when merging edges.
To use focused proofreading outside of Raveler, examine the functions used to evaluate GT in neuroproof_graph_analyze_gt. In particular, run_body and run_synapse functions show how to use neuroproof to choose the most important edge and how the thresholds are set.
The easiest way to use focused proofreading is probably the following:
- Generate a graph file by running something like neuroproof_graph_predict (or the workflow in DVIDSparkServices)
- Choose a set of important bodies in a segmentation (e.g., big bodies or bodies that contain synapse points)
- For each important body, use Disjkstra's algorithm to find the closest bodies and consider paths whose endpoints are within the important body set and whose affinity is reasonably high.
Neuroproof has a simple implementation of Dijsktra's algorithm that will work over the neuroproof output. See example below
% from neuroproof import FocusedProofreading
% graph = FocusedProofreading.Graph("graph.json")
% BODYID = 1
% PATHCUTOFF = 0
% THRES = 0.1
% close_bodies = graph.find_close_bodies(BODYID, PATHCUTOFF, THRES)
% #This will return something like
% [2,6,3,0.4]
Where this gives a path 1-3-6-2 with an affinity between 1 and 2 of 0.40. PATHCUTOFF specifies the length of path that will be considered (0 is infinite to within the constraint). THRES is the minimum acceptable affinity (0.1 or 10% here).
The top-level programs that are built in NeuroProof are defined in the src/ directory. NeuroProof's capabilities are mainly accessed via command-line executables. A subset of these capabilities are exposed in a python interface. For some examples on how to run the tool, please consult the 'examples' directory. If you have just cloned the NeuroProof repository, you will need to add the example submodule. (The examples are not included by default due to the size of the directory.)
% git submodule init
% git submodule update
- Update algorithms based on latest Fly EM research
- Add more library/service capabilities (especially for generating metrics)
- Direct support for 2D datasets (2D data is generally allowable in the current implementation)
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