NA-MIC Brains Collaboration

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Abstract

This project is not an NCBC collaboration grant, but instead a Continued Development and Maintenance of Software grant. The intent of this application is to update the BRAINS image analysis software developed at the University of Iowa.

Grant #

1R01NS050568

Key Personnel

The collaborators include Vincent Magnotta, Hans Johnson, Jeremy Bockholt, and Nancy Andreasen.

Projects

There are three main thrusts of this application as outlined below.

Implement an Automated Brain Analysis Pipeline

• BRAINS Automated Pipeline
• Support for Batch Processing
• Visual Review and Verification of Results

Code Refactoring for Cross Platform Support

• Refactor Current Modules using ITK and VTK
• Build GUI using Slicer3 Interface
• Use Python as Scripting Language

Validation of Pipeline Using MIND MCIC Sample

• Evaluate MIND MCIC Human Phantom Sample Using Automated Pipeline
• Evaluate Differences between Patients with Schizophrenia and Normal Controls using Automated Pipeline

Tools Developed

Here is a list of tools that are being developed as part of this project. A number of these tools have been contributed to the NITRC website. All of the tools are built upon the Slicer3 execution model and therefore will readily plug-in to Slicer3.

• BRAINSFit - Linear fitting of two 3D image sets using a mutual information similarity metric
• BRAINSDemonsWarp - Non-linear registration of two image sets. This includes the diffeomorphic demons algorithm along with a number of useful enhancements.
• BRAINSMush - Tool to combine T1 and T2 datasets into a composite image. The resulting image is then used to perform brain extraction.
• Neural Networks - Automated segmentation of subcortical and cerebellar regions of interest using an artificial neural network.
• BRAINSTracer - Tracing tool that uses thge vtkContourWidget for defining a region of interest manually
• GTRACT - Diffusion tensor analysis and Fiber tracking tool
• BRAINS Auto Workup - Fully automated image analysis pipeline for generation of volumetric measurements.

BRAINS Features

• BRAINS Gallery
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  BRAINS Surface showing cortical depth

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  BRAINS Surface showing curvature (ie sulcal and gyral labels)

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  BRAINS Continuous Tissue Classification

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  Automated segmentation of Subcortical nuclei using Neural Networks

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  BRAINS Image Registration using ITK Mutual Information

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  Lesion identification with FLAIR, T1 and T2 weighted images

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  Talairach lobar labels

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  GTRACT DTI fiber tracking

Publications

1. 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. 2007 Aug 22; [Epub ahead of print].