Projects:RegistrationLibrary:RegLib C11

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

					LEGEND this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution this indicates the moving image that determines the registration transform. 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.
Target Atlas T1	Target Atlas Labelmap		Moving Atlas T1	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 registration, thalamic nuclei

Input Data

• reference/fixed : T1w coronal, 1mm isotropic. Called A1_gray
• reference/fixed : labelmap , aligned with above. Called A1_label
• moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called A0_gray
• 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

Download

• download entire tutorial data package (Original Data, Intermediate Results, Solution, zip file 72 MB)
• download old atlas dataset (MRI+labelmap,thalamic nuclei models, list of labels+names), zip file 5.4 MB)
• download new atlas dataset (MRI+labelmap, thalamus model), zip file 5.4 MB)
• download result new merged atlas dataset (grayscale, new labelmap incl. thalamic nuclei, result transform mapping old to new, text (csv) file with label names & numbers'), zip file 66 MB)
• download full step-by-step tutorial (PowerPoint, zip file, 2.5 MB)
• download compare set (New Atlas, 2 merged versions (full & clipped), thalamus models, Xform, Merged colormap, zip file, 67 MB)

Link to User Guide: How to Load/Save Registration Parameter Presets

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.