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

From Slicer Wiki
Jump to: navigation, search
m (Text replacement - "\[http:\/\/www\.slicer\.org\/slicerWiki\/index\.php\/([^ ]+) ([^]]+)]" to "$2")
 
(6 intermediate revisions by 2 users not shown)
Line 3: Line 3:
 
=Description=
 
=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:
 
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 [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 [[Modules:N4ITKBiasFieldCorrection-Documentation-3.6|N4ITKBiasFieldCorrection]] (Tustison et al 2010)
 
* Step 2: Register the atlas to the MRI scan via [[Modules:BRAINSFit| BRAINSFit]] (Johnson et al 2007)
 
* Step 2: Register the atlas to the MRI scan via [[Modules:BRAINSFit| BRAINSFit]] (Johnson et al 2007)
 
* Step 3: Compute the intensity distributions for each structure <BR>
 
* Step 3: Compute the intensity distributions for each structure <BR>
 
Compute intensity distribution (mean and variance) for each label 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.
 
Compute intensity distribution (mean and variance) for each label 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)
+
* Step 4: Automatically segment the MRI scan into the brain tissue classes using [[Modules:EMSegmenter-3.6|EM Algorithm]]  (Pohl et al 2007)
 +
* Step 5:Further parcellate the tissue classes into hemispheres using the aligned parcellation maps
  
 
=Anatomical Tree=
 
=Anatomical Tree=
 
+
Tree used by the EMSegmenter for segmenting the images into the major tissue classes
 
* root
 
* root
 
** background (BG)
 
** background (BG)
Line 41: Line 42:
 
atlas_whitematter_hem and atlas_greymatter_hem to divide the segmentation into left and right regions.
 
atlas_whitematter_hem and atlas_greymatter_hem to divide the segmentation into left and right regions.
  
<gallery perrow=1: widths=630px : heights=210px>
+
<gallery perrow=2: widths=210px : heights=210px>
 
Image:EMS_Parcellation_map_atlas_whitematter_hem.png
 
Image:EMS_Parcellation_map_atlas_whitematter_hem.png
 
Image:EMS_Parcellation_map_atlas_greymatter_hem.png
 
Image:EMS_Parcellation_map_atlas_greymatter_hem.png
Line 48: Line 49:
 
=Result=
 
=Result=
 
<gallery perrow=1: widths=630px : heights=210px>
 
<gallery perrow=1: widths=630px : heights=210px>
Image:MRI-Human-Brain-T1.png
+
Image:EMS_Hemisphere_orig.png
Image:MRI-Human-Brain-Labelmap.png
+
Image:EMS_Hemisphere_labelmap.png
 
</gallery>
 
</gallery>
  

Latest revision as of 02:30, 27 November 2019

Home < EMSegmenter-Tasks:MRI-Human-Brain-Hemisphere

Return to EMSegmenter Task Overview Page

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 (Johnson et al 2007)
  • Step 3: Compute the intensity distributions for each structure

Compute intensity distribution (mean and variance) for each label 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 brain tissue classes using EM Algorithm (Pohl et al 2007)
  • Step 5:Further parcellate the tissue classes into hemispheres using the aligned parcellation maps

Anatomical Tree

Tree used by the EMSegmenter for segmenting the images into the major tissue classes

  • 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

EMSegmenter MRI-Human-Brain Template 420x420.png EMSegmenter MRI-Human-Brain CSF 420x420.png EMSegmenter MRI-Human-Brain GM 420x420.png EMSegmenter MRI-Human-Brain WM 420x420.png
Template (T1) CSF GM WM

This task is using two parcellation maps: atlas_whitematter_hem and atlas_greymatter_hem to divide the segmentation into left and right regions.

Result

Acknowledgment

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

Citations