Projects:RegistrationLibrary:RegLib C11

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

Input

Target Atlas T1	Target Atlas Labelmap		Moving Atlas T1	Moving Atlas Labelmap

Modules

'Slicer 3.6.1 recommended modules: Surface Registration

Download

• Data
  • download entire tutorial package (Original Data, Intermediate Results, Solution, zip file 74 MB)
  • download old atlas dataset (MRI+labelmap,thalamic nuclei models, list of labels+names), zip file 5.4 MB)
  • download new atlas dataset (MRI T1+T2, labelmap, thalamus model), zip file 126MB)
  • download result merged new atlas dataset (MRI T1+T2, new merged labelmaps (2x) incl. thalamic nuclei, colormap file with label names), zip file 132 MB)
  • download resampled atlas dataset (T1 only resampled to 1mm isotropic, padded to 256 , labelmap reoriented as sagittal), zip file 9 MB)
• Documentation
  • download full step-by-step tutorial (PowerPoint file, 2.5 MB)
  • download full step-by-step tutorial (PDF file, 2.8 MB)

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

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.