Difference between revisions of "BRAINS Automated Pipeline"

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<font color=green> green = In progress </font>
<font color=green> red = To be done </font>
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<font color=red> red = To be done </font>
  
  

Revision as of 21:00, 10 December 2008

Home < BRAINS Automated Pipeline

Objective:

Develop tools to eliminate the need for manual intervention for the analysis of brain morphology using MR images. We currently have a semiautomated pipeline that includes the following steps:

  • AC-PC Alignment of T1 Volume
  • Co-registration of T2 weighted images to AC-PC Aligned T1
  • Bias Field Correction
  • Tissue Classification
  • Talairach Atlas Segmentation
  • Neural Network Segmentation
  • Surface Generation
  • Measurements of Volumetric and Surface Data


Proposed Pipeline:

  • Alignment of T2 to raw T1 scan - Use BRAINSFit
  • Brain Extraction - BRAINSMush
  • Tissue Classification - KMeans + Bayesian
  • Bias Field Correction - MRBiasCorrector
  • AC-PC Detection T1 - ART
  • Resample T1 Image - TBD
  • AC-PC Detection T2 - ART
  • Alignment of T2 to AC-PC aligned T1 - BrainsFit Initialized with ART results
  • Brain Extraction - BRAINSMush + BRAINSCut
  • Tissue Classification - TBD
  • Talairach Brain Labeling - TBD
  • Neural Network Labeling - BRAINSCut
  • Measurements - TBD
  • Surface Generation - BrainSurf
  • Surface Labeling - TBD


blue = Complete

green = In progress

red = To be done


Progress:

  • A beta version of the automated workup exists within BRAINS2.
  • BRAINS3 has been created using a TCL command line to connect components from BRAINS2, ITK, and VTK
    • Nearly all code is in ITK with a couple of exceptions: ROIs and Talairach Parameters
    • Interfaces have been created to seamlessly handle BRAINS and ITK images using ITK filters
    • Requires the WrapITK Interface
  • Added a few new classes required for wrapping and utilization via the command line
  • Added improved interpolation schemes including Sinc and B-Spline
    • Will become the standard for images used for tissue classification


To Do:

  • Refine data types used in WrapITK to decrease the memory footprint
  • Improve TCL scripts to verify that memory is being freed appropriately
  • Look into loading only certain aspects of ITK when they are needed
  • Implement Tissue classification in ITK
  • Handle Talairach parameters using VTK structured grids
  • Use new ROI format, used by BRAINSTracer, for working with ROIs
  • Integrate some features through command line with BRAINSTracer


Key Investigators:

  • University of Iowa: Vincent Magnotta, Hans Johnson, Greg Harris, Wen Li, Steven Dunn, Nancy Andreasen
  • The MIND Institute: Jeremy Bockholt


Links:

Papers:

  1. Magnotta VA, Harris G, Andreasen NC, O'Leary DS, Yuh WT, Heckel D. Structural MR image processing using the BRAINS2 toolbox. Comput Med Imaging Graph. 26(4):251-64, 2002.
  2. Harris G, Andreasen NC, Cizadlo T, Bailey JM, Bockholt HJ, Magnotta VA, Arndt S. Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection. J Comput Assist Tomogr. 23(1):144-54, 1999.
  3. Magnotta VA, Andreasen NC, Schultz SK, Harris G, Cizadlo T, Heckel D, Nopoulos P, Flaum M. Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging. Cereb Cortex. 9(2):151-60, 1999.
  4. 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.
  5. 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.