Difference between revisions of "BRAINS Automated Pipeline"
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Revision as of 21:00, 10 December 2008
Home < BRAINS Automated PipelineObjective:
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:
- 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.
- 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.
- 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.
- 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.