Difference between revisions of "BRAINSCut"
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===Figures=== | ===Figures=== | ||
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Image:BRAINS-ANN-Brain-Subject1-T1.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T1 for Subject 1 | Image:BRAINS-ANN-Brain-Subject1-T1.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T1 for Subject 1 | ||
Image:BRAINS-ANN-Brain-Subject1-T2.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 1 | Image:BRAINS-ANN-Brain-Subject1-T2.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 1 | ||
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Image:BRAINS-ANN-Brain-Subject3-T2.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 3 | Image:BRAINS-ANN-Brain-Subject3-T2.png|Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 3 | ||
</gallery> | </gallery> | ||
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===Usage=== | ===Usage=== |
Latest revision as of 21:53, 10 December 2008
Home < BRAINSCutBack to NA-MIC Brains Collaboration
Summary
BRAINSCut is a software package for segmentation of structures using artificial neural networks. Currently this tool supports the segmentation of the following structures: brain, caudate, putamen, thalamus, hippocampus, anterior cerebellum, interior posterior cerebellum, superior posterior cerebellum, corpus medullary. Future regions will include the globus pallidus, amygdala, and nucleus accumbens. The command line uses the Slicer3 execution model framework.
Progress
- Integration with a high dimensional registration to the atlas probability map
- Improved thresholding of the output activation maps
- Code added to NITRC
- Coupled Neural network with MUSH Brain to generate a brain mask without requiring tissue classification
To Do
- Complete integration with the FANN library
- Link to a BSD style neural network library
- Look at the ability to use for segmentation of cortical regions
Key Investigators
- University of Iowa: Hans Johnson, Ronald Pierson, Kent Williams, Greg Harris, Vincent Magnotta
Figures
Usage
BRAINSCut [--processinformationaddress <std::string>] [--xml] [--echo] [--applyModel] [--trainModel] [--createVectors] [--generateProbability] [--trainModelStartIndex <int>] [--netConfiguration <std::string>] [--] [--version] [-h] Description: Automatic Segmentation using neural networks Author(s): Vince Magnotta, Hans Johnson, Greg Harris, Kent Williams
Where:
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0) --xml Produce xml description of command line arguments (default: 0) --echo Echo the command line arguments (default: 0) --applyModel apply the neural net (default: 0) --trainModel train the neural net (default: 0) --createVectors create vectors for training neural net (default: 0) --generateProbability Generate probability map (default: 0) --trainModelStartIndex <int> Starting iteration for training (default: 0) --netConfiguration <std::string> XML File defining AutoSegmentation parameters --, --ignore_rest Ignores the rest of the labeled arguments following this flag. --version Displays version information and exits. -h, --help Displays usage information and exits.
Links
Papers
- Powell S, Magnotta VA, Johnson H, Jammalamadaka VK, Pierson R, Andreasen NC. Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. Neuroimage. 39(1):238-47, 2008.
- Magnotta VA, Heckel D, Andreasen NC, Cizadlo T, Corson PW, Ehrhardt JC, Yuh WT. Measurement of brain structures with artificial neural networks: two- and three-dimensional applications. Radiology. 211(3):781-90, 1999.