BRAINSCut

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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:

1. Integration with a high dimensional registration to the atlas probability map
2. Improved thresholding of the output activation maps
3. Code added to NITRC
4. Coupled Neural network with MUSH Brain to generate a brain mask without requiring tissue classification

To Do:

1. Complete integration with the FANN library
2. Link to a BSD style neural network library
3. 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:

• BRAINSTracer
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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T1 for Subject 1

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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 1

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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T1 for Subject 2

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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 2

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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T1 for Subject 3

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  Brain Mask generated using BRAINSMush image and BRAINSCut overlaid on T2 for Subject 3

Usage:

Links:

• University of Iowa Department of Psychiatry
• University of Iowa MRI Center

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.