Projects:RegistrationLibrary:RegLib C11

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v3.6.1 Slicer3-6Announcement-v1.png Slicer Registration Library Case #11:
Co-registration of two anatomic brain atlases for merging of structure labels

Input

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 this image
Target Atlas T1 Target Atlas Labelmap Moving Atlas T1 Moving Atlas Labelmap

Modules

'Slicer 3.6.1 recommended modules: Surface Registration

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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 registration, thalamic nuclei

Input Data

  • fixed : T1w coronal, 1mm isotropic. Called A1_gray; 1mm isotropic, 256 x 256 x 256
  • fixed : labelmap , aligned with above. Called A1_label; 1mm isotropic, 256 x 256 x 256
  • moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called A0_gray; 0.9375 x 0.9375 x 1.5mm isotropic, 256 x 256 x 159
  • moving: labelmap , aligned with above. Called A0_label; 0.9375 x 0.9375 x 1.5mm isotropic, 256 x 256 x 159

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 ICP Surface Registration + Clipping


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.
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