Difference between revisions of "EMSegmenter-Tasks:MRI-Human-Brain"

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* Step 1: Perform image inhomogeneity correction of the MRI scan via [http://www.slicer.org/slicerWiki/index.php/Modules:N4ITKBiasFieldCorrection-Documentation-3.6 N4ITKBiasFieldCorrection] (Tustison et al 2010)
 
* Step 1: Perform image inhomogeneity correction of the MRI scan via [http://www.slicer.org/slicerWiki/index.php/Modules:N4ITKBiasFieldCorrection-Documentation-3.6 N4ITKBiasFieldCorrection] (Tustison et al 2010)
 
* Step 2: Register the atlas to the MRI scan via [[Modules:BRAINSFit| BRAINSFit]] (ask Hans for citation)
 
* Step 2: Register the atlas to the MRI scan via [[Modules:BRAINSFit| BRAINSFit]] (ask Hans for citation)
* Step 3: Compute the intensity distributions for each structure
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* Step 3: Compute the intensity distributions for each structure <BR>
* Step 4: Automatically segment the MRI scan into the structures of interest using the [[Modules:EMSegmenter-3.6|EM Algorithm]]  (Pohl et al 2007)
+
Compute mean intensity distribution and variance by automatically sampling from the MR scan. The sampling for a specific label is constrained to the region that consists of voxels with high probability (top 95%) of being assigned to the label according to the aligned atlas.
 +
* Step 4: Automatically segment the MRI scan into the structures of interest using [[Modules:EMSegmenter-3.6|EM Algorithm]]  (Pohl et al 2007)
  
 
==Anatomical Tree==
 
==Anatomical Tree==

Revision as of 01:05, 1 December 2010

Home < EMSegmenter-Tasks:MRI-Human-Brain

MRI Human Brain

Description

Single channel automatic segmentation of t1w-MRI brain scans into the major tissue classes (gray matter, white matter, csf). The task can only be applied to t1w brain scan showing parts of the skull and neck. The pipeline consist of the following steps:

  • Step 1: Perform image inhomogeneity correction of the MRI scan via N4ITKBiasFieldCorrection (Tustison et al 2010)
  • Step 2: Register the atlas to the MRI scan via BRAINSFit (ask Hans for citation)
  • Step 3: Compute the intensity distributions for each structure

Compute mean intensity distribution and variance by automatically sampling from the MR scan. The sampling for a specific label is constrained to the region that consists of voxels with high probability (top 95%) of being assigned to the label according to the aligned atlas.

  • Step 4: Automatically segment the MRI scan into the structures of interest using EM Algorithm (Pohl et al 2007)

Anatomical Tree

  • root
    • background (BG)
      • air (AIR)
      • skull (skull)
    • intracranial cavity (ICC)
      • white matter (WM)
      • grey matter (GM)
      • cerebrospinal fluid (CSF)

Atlas

Atlas was generated based on 82 scans and corresponding segmentations provided by Psychiatry Neuroimaging Laboratory, BWH. We registered the scans to a preselected template via Warfield et al. 2001.
Image Dimension = 256 x 256 x 124
Image Spacing = 0.9375 x 0.9375 x 1.5
Dominique show example of atlas (T1, wm , gm ,csf)

Result

MRI-Human-Brain-T1.png MRI-Human-Brain-Labelmap.png

Acknowledgment

The construction of the pipeline was supported by funding from NIH NCRR 2P41RR013218 Supplement.

Citations

  • Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC N4ITK: Improved N3 Bias Correction, IEEE Trans Med Imag, 2010
  • Pohl K, Bouix S, Nakamura M, Rohlfing T, McCarley R, Kikinis R, Grimson W, Shenton M, Wells W. A Hierarchical Algorithm for MR Brain Image Parcellation. IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.
  • S. Warfield, J. Rexilius, P. Huppi, T. Inder, E. Miller, W. Wells, G. Zientara, F. Jolesz, and R. Kikinis, “A binary entropy measure to assess nonrigid registration algorithms,” in MICCAI, LNCS, pp. 266–274, Springer, October 2001.