BRAINS Automated Pipeline
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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
Progress:
- A beta version of the automtated workup exists within BRAINS2. We are currently working on building the BRAINS3 command liner interface and the BRAINS2 modules will be ported to BRAINS3 once the TCL commnad line interface exists.
Key Investigators:
- University of Iowa: Vincent Magnotta, Hans Johnson, 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.
