Difference between revisions of "Projects:RegistrationLibrary:RegLib C11"

From NAMIC Wiki
Jump to: navigation, search
Line 39: Line 39:
 
*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1w coronal, 1mm isotropic. Called ''A1_gray''
 
*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1w coronal, 1mm isotropic. Called ''A1_gray''
 
*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : labelmap , aligned with above. Called ''A1_label''
 
*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : labelmap , aligned with above. Called ''A1_label''
−
*[[Image:Button_green_moving_white.jpg|20px]] moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called ''A0_gray''
+
*[[Image:Button_blue_tag_white.jpg|20px]] moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called ''A0_gray''
 
*[[Image:Button_green_moving_white.jpg|20px]] moving: labelmap , aligned with above. Called ''A0_label''
 
*[[Image:Button_green_moving_white.jpg|20px]] moving: labelmap , aligned with above. Called ''A0_label''
 
=== Methods ===
 
=== Methods ===
 
#Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
 
#Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
 
#Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
 
#Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
−
#Build label mask of thalamus for A0: threshold A0_label from 500 t0 526
+
#Build label mask of thalamus for A0: '''Editor''' module
−
#Build label mask of thalamus for A1: change label 10 to 49; threshold A1_label from 49 to 49
+
##Create Labelmap From”: ''A0_labels''
−
#Smooth A1 thalamus labelmap: Median Filter
+
##Select Labelmap to Edit: select the newly created “A0_labels-label” and then select “Rename”. Rename the new volume to ''A0_thalamus''
−
#Build thalamus surface model of A0
+
##From the icon panel, select the “Threshold Icon”
−
#Build thalamus surface model of A1
+
##Threshold range: enter 500 and 526
−
#run '''Python Surface ICP Registration''' , Settings:  
+
#change ''A1_labels'': change label 10 to 49; '''Editor''' module
−
::*Landmark transform mode: Affine
+
##Select Labelmap to Edit: ''Aa_labels''
−
::*Mean Distance Mode: RMS
+
##Label field: enter 49
−
::*Maximum Number of Iterations: 500
+
##Change Island Icon; left click on area with left thalamus label 10
−
::*Maximum Nmber of Landmarks: 200
+
#Build label mask of thalamus for A1: '''Editor''' module
−
::*Start by matching centroids:  yes
+
##Create Labelmap From”: ''A1_labels''
−
::*maximum mean distance:  0.01
+
##Select Labelmap to Edit: select the newly created “A1_labels-label” and then select “Rename”. Rename the new volume to ''A1_thalamus''
−
::*Initial Transform: none
+
##From the icon panel, select the “Threshold Icon”
−
::*Input Surface: ''A0_ThalamusModel''
+
##Threshold range: enter 49 and 49
−
::*Target Surface: ''A1_ThalamusModel''
+
#Smooth A1 thalamus labelmap: '''Filtering / Denoising / Median Filter'''
−
::*Output Surface: none
+
##Neighborhood Size: (default) 1,1,1
−
::*Output Transform: ''Xform_A0Affine_ICP''
+
##Input Volume: A1_thalamus
 +
##Output Volume: A1_thalamus
 +
#Build thalamus surface model of A0: '''Model Maker''' module
 +
##Input Volume: ''A0_thalamus''
 +
##Model Name: ''A0_ThalamusModel''
 +
##Labels: 1
 +
##Smooth: 50
 +
##Decimate: 0.25
 +
##Split Normals: no
 +
##Point Normals: yes
 +
##Save Intermediate Models: no
 +
#Build thalamus surface model of A1 ('''Model Maker''' module as above, with ''A1_thalamus'' as input and ''A1_ThalamusModel'' as output.
 +
#Co-register thalamus surfaces: '''Python Surface ICP Registration''' module.  Settings:  
 +
##Landmark transform mode: Affine
 +
##Mean Distance Mode: RMS
 +
##Maximum Number of Iterations: 500
 +
##Maximum Nmber of Landmarks: 200
 +
##Start by matching centroids:  yes
 +
##maximum mean distance:  0.01
 +
##Initial Transform: none
 +
##Input Surface: ''A0_ThalamusModel''
 +
##Target Surface: ''A1_ThalamusModel''
 +
##Output Surface: none
 +
##Output Transform: ''Xform_A0Affine_ICP''
 
# Apply Registration Xform to labelmap: '''Resample Scalar/Vector/DWI Volume''' module. Settings:  
 
# Apply Registration Xform to labelmap: '''Resample Scalar/Vector/DWI Volume''' module. Settings:  
−
::*Input Volume : ''A0_labels''
+
##Input Volume : ''A0_labels''
−
::*Reference Volume : ''A1_labels''
+
##Reference Volume : ''A1_labels''
−
::*Output Volume : “Create New Volume”, rename to “A0_labels_aff”
+
##Output Volume : “Create New Volume”, rename to “A0_labels_aff”
−
::*Transform Node: ''Xform_A0Affine_ICP''
+
##Transform Node: ''Xform_A0Affine_ICP''
−
::*Interpolation Type: “nn”  
+
##Interpolation Type: “nn”  
 
##Change datatype of new volume ''A0_labels_aff'' to labelmap ('''Volumes''' module)
 
##Change datatype of new volume ''A0_labels_aff'' to labelmap ('''Volumes''' module)
 
# Apply Registration Xform to thalamus mask: '''Resample Scalar/Vector/DWI Volume''' module. Settings:  
 
# Apply Registration Xform to thalamus mask: '''Resample Scalar/Vector/DWI Volume''' module. Settings:  
−
::*Input Volume : ''A0_thalamus''
+
##Input Volume : ''A0_thalamus''
−
::*Reference Volume : ''A1_labels''
+
##Reference Volume : ''A1_labels''
−
::*Output Volume : “Create New Volume”, rename to ''A0_thalamus_aff''
+
##Output Volume : “Create New Volume”, rename to ''A0_thalamus_aff''
−
::*Transform Node: ''Xform_A0Affine_ICP''
+
##Transform Node: ''Xform_A0Affine_ICP''
−
::*Interpolation Type: “nn”  
+
##Interpolation Type: “nn”  
 
##Change datatype of new volume  ''A0_labels_aff'' to labelmap ('''Volumes''' module)
 
##Change datatype of new volume  ''A0_labels_aff'' to labelmap ('''Volumes''' module)
 
#Mask new labelmap with ''A0_thalamus_aff'': '''Mask Image''' module
 
#Mask new labelmap with ''A0_thalamus_aff'': '''Mask Image''' module
−
::*Input Volume : ''A0_labels_aff''
+
##Input Volume : ''A0_labels_aff''
−
::*Mask Volume : ''A0_thalamus_aff''�
+
##Mask Volume : ''A0_thalamus_aff''  
−
::*Masked Volume: ''A0_labels aff''
+
##Masked Volume: ''A0_labels aff''
 
##Mask new labelmap with A1_thalamus: '''Mask Image''' module
 
##Mask new labelmap with A1_thalamus: '''Mask Image''' module
−
::*Input Volume : ''A0_labels_aff''�
+
##Input Volume : ''A0_labels_aff''
−
::*Mask Volume : ''A1_thalamus_aff''�
+
##Mask Volume : ''A1_thalamus_aff''  
−
::*Masked Volume: New volume, rename to: ''A0_labels aff_clip''
+
##Masked Volume: New volume, rename to: ''A0_labels aff_clip''
 
#Typecast A1_labelmap: '''Cast Image''' module
 
#Typecast A1_labelmap: '''Cast Image''' module
−
::*Input Volume: ''A1_label''
+
##Input Volume: ''A1_label''
−
::*Output Volume: ''A1_label''
+
##Output Volume: ''A1_label''
−
::*Output Type: “short”
+
##Output Type: “short”
 
#Merge Labelmaps:'''Image Label Combine''' module
 
#Merge Labelmaps:'''Image Label Combine''' module
−
::*Input Label Map A: ''A0_label_aff_clip''
+
##Input Label Map A: ''A0_label_aff_clip''
−
::*Input Label Map B: ''A1_labels''
+
##Input Label Map B: ''A1_labels''
−
::*Output Label Map: “Create New Volume”, rename to ''A1_labels_merged''
+
##Output Label Map: “Create New Volume”, rename to ''A1_labels_merged''
−
::*First label overwrites second: yes
+
##First label overwrites second: yes
−
 
 
  
 
=== Registration Results===
 
=== Registration Results===
Line 121: Line 143:
  
 
=== Discussion: Registration Challenges ===
 
=== Discussion: Registration Challenges ===
−
*Because the atlas represents a probabilistic image (i.e. contains blurring from combining multiple subjects), its contrast differs significantly from the target image.
+
*Because the structures of interest are a very small subset of the image without distinct grayscale contrast
−
*The atlas has strong rotational misalignment that can cause difficulty for automated affine registration.
+
*the two atlases represent different anatomies and hence some residual misalignment is inevitable
−
*The two images represent different anatomies, a non-rigid registration is required
+
*the two labelmaps have different resolutions and different smoothness of structure outlines. Some need filtering to remove spurious surface details that would distract the registration algorithm
  
 
=== Discussion: Key Strategies ===
 
=== Discussion: Key Strategies ===
−
*Because of the strong differences in image contrast, Mutual Information is recommended as the most robust metric.
+
*Because the structures of interest are a very small subset of the image without distinct grayscale contrast, we co-register surfaces rather than intensity volumes
−
*masking (skull stripping) is highly recommended to obtain good results.
+
 
−
*because speed is not that critical, we increase the sampling rate for both affine and BSpline registration
 
−
*we also expect larger differences in scale & distortion than with regular structural scans: so we significantly  (2x-3x) increase the expected values for scale and skew from the defaults.
 
−
*a good affine alignment is important before proceeding to non-rigid alignment to further correct for distortions.
 
  
 
=== Acknowledgments ===
 
=== Acknowledgments ===
−
*dataset provided by Killian Pohl, Ph.D.
+
*dataset provided by Ron Kikinis, M.D.  and Florin Talos, M.D.

Revision as of 17:12, 17 February 2010

Home < Projects:RegistrationLibrary:RegLib C11

Back to ARRA main page
Back to Registration main page
Back to Registration Use-case Inventory

Slicer Registration Library Exampe #11: Co-registration of two anatomic brain atlases for merging of structure labels

this is the fixed reference image. All images are aligned into this space this is the fixed target, a label-map in the same space as the fixed reference. The registration target is derived from this image lleft this is the moving image, but the transform is calculated by matching surfaces derived from the labelmaps this is the moving image to which the calculated transform is applied. The model surfaces used to calculate the transform are derived from the two labelmaps LEGEND

lleft this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution
lleft this indicates the moving image that determines the registration transform.
lleft this indicates images that passively move into the reference space, i.e. they have the transform applied but do not contribute to the calculation of the transform.

lleft Target Atlas T1 lleft Target Atlas Labelmap lleft Moving Atlas T1 lleft Moving Atlas Labelmap
1mm isotropic
256 x 256 x 256
PA 		1mm isotropic
256 x 256 x 256
PA 		0.9375 x 0.9375 x 1.5mm isotropic
256 x 256 x 159
PA 		0.9375 x 0.9375 x 1.5mm isotropic
256 x 256 x 159
PA


Objective / Background

This is an example of inter-subject registration via surface matching. The structures of interest are a small subset of the entire image, hence registration is not driven by image intensities but rather two model surfaces derived from the labelmaps.

Keywords

MRI, brain, head, inter-subject, atlas to atlas, surface-based segmentation, thalamic nuclei

Input Data

  • Button red fixed white.jpgreference/fixed : T1w coronal, 1mm isotropic. Called A1_gray
  • Button red fixed white.jpgreference/fixed : labelmap , aligned with above. Called A1_label
  • Button blue tag white.jpg moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called A0_gray
  • Button green moving white.jpg moving: labelmap , aligned with above. Called A0_label

Methods

  1. Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
  2. Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
  3. Build label mask of thalamus for A0: Editor module
    1. Create Labelmap From”: A0_labels
    2. Select Labelmap to Edit: select the newly created “A0_labels-label” and then select “Rename”. Rename the new volume to A0_thalamus
    3. From the icon panel, select the “Threshold Icon”
    4. Threshold range: enter 500 and 526
  4. change A1_labels: change label 10 to 49; Editor module
    1. Select Labelmap to Edit: Aa_labels
    2. Label field: enter 49
    3. Change Island Icon; left click on area with left thalamus label 10
  5. Build label mask of thalamus for A1: Editor module
    1. Create Labelmap From”: A1_labels
    2. Select Labelmap to Edit: select the newly created “A1_labels-label” and then select “Rename”. Rename the new volume to A1_thalamus
    3. From the icon panel, select the “Threshold Icon”
    4. Threshold range: enter 49 and 49
  6. Smooth A1 thalamus labelmap: Filtering / Denoising / Median Filter
    1. Neighborhood Size: (default) 1,1,1
    2. Input Volume: A1_thalamus
    3. Output Volume: A1_thalamus
  7. Build thalamus surface model of A0: Model Maker module
    1. Input Volume: A0_thalamus
    2. Model Name: A0_ThalamusModel
    3. Labels: 1
    4. Smooth: 50
    5. Decimate: 0.25
    6. Split Normals: no
    7. Point Normals: yes
    8. Save Intermediate Models: no
  8. Build thalamus surface model of A1 (Model Maker module as above, with A1_thalamus as input and A1_ThalamusModel as output.
  9. Co-register thalamus surfaces: Python Surface ICP Registration module. Settings:
    1. Landmark transform mode: Affine
    2. Mean Distance Mode: RMS
    3. Maximum Number of Iterations: 500
    4. Maximum Nmber of Landmarks: 200
    5. Start by matching centroids: yes
    6. maximum mean distance: 0.01
    7. Initial Transform: none
    8. Input Surface: A0_ThalamusModel
    9. Target Surface: A1_ThalamusModel
    10. Output Surface: none
    11. Output Transform: Xform_A0Affine_ICP
  10. Apply Registration Xform to labelmap: Resample Scalar/Vector/DWI Volume module. Settings:
    1. Input Volume : A0_labels
    2. Reference Volume : A1_labels
    3. Output Volume : “Create New Volume”, rename to “A0_labels_aff”
    4. Transform Node: Xform_A0Affine_ICP
    5. Interpolation Type: “nn”
    6. Change datatype of new volume A0_labels_aff to labelmap (Volumes module)
  11. Apply Registration Xform to thalamus mask: Resample Scalar/Vector/DWI Volume module. Settings:
    1. Input Volume : A0_thalamus
    2. Reference Volume : A1_labels
    3. Output Volume : “Create New Volume”, rename to A0_thalamus_aff
    4. Transform Node: Xform_A0Affine_ICP
    5. Interpolation Type: “nn”
    6. Change datatype of new volume A0_labels_aff to labelmap (Volumes module)
  12. Mask new labelmap with A0_thalamus_aff: Mask Image module
    1. Input Volume : A0_labels_aff
    2. Mask Volume : A0_thalamus_aff
    3. Masked Volume: A0_labels aff
    4. Mask new labelmap with A1_thalamus: Mask Image module
    5. Input Volume : A0_labels_aff
    6. Mask Volume : A1_thalamus_aff
    7. Masked Volume: New volume, rename to: A0_labels aff_clip
  13. Typecast A1_labelmap: Cast Image module
    1. Input Volume: A1_label
    2. Output Volume: A1_label
    3. Output Type: “short”
  14. Merge Labelmaps:Image Label Combine module
    1. Input Label Map A: A0_label_aff_clip
    2. Input Label Map B: A1_labels
    3. Output Label Map: “Create New Volume”, rename to A1_labels_merged
    4. First label overwrites second: yes

Registration Results

unregistered
after BSpline non-rigid registration

Download

Discussion: Registration Challenges

  • Because the structures of interest are a very small subset of the image without distinct grayscale contrast
  • the two atlases represent different anatomies and hence some residual misalignment is inevitable
  • the two labelmaps have different resolutions and different smoothness of structure outlines. Some need filtering to remove spurious surface details that would distract the registration algorithm

Discussion: Key Strategies

  • Because the structures of interest are a very small subset of the image without distinct grayscale contrast, we co-register surfaces rather than intensity volumes


Acknowledgments

  • dataset provided by Ron Kikinis, M.D. and Florin Talos, M.D.
Retrieved from "https://www.na-mic.org/w/index.php?title=Projects:RegistrationLibrary:RegLib_C11&oldid=48901"