How to use

1. Auto3dgm UI in Slicer

The auto3dgm UI consists of three components:

2. Download example data and load data in Slicer

Download example data from

In the setup tab, specify

Then click on Load Data.

Next, click on Check Mesh Quality, which finds if there is any NaN-valued vertex in the mesh. If the quality check failed, make sure the data is cleaned before you proceed to the next step.

3. Set up parameters

Auto3dgm uses a two-phase analysis for shape alignment. The algorithm first subsamples two sets of pseudolandmarks for each shape, and then aligns the subsampled points. In the algorithm, we will need to specify how many points we will subsample in each phase. The default is set to be 10 points for phase 1 and 40 points for phase 2. In general, a few hundred points will give pretty good shape alignment. The more points we use, the better the results.

There are some other parameters in the auto3dgm UI. In general, you can leave them as they are.

4. Run auto3dgm

In the run tab, you can either run the entire analysis or run each step separately. Here you can simply click “run all steps”. The aligned meshes (in .ply format) and the registered pseudolandmarks (in .fcsv files) will be saved in the specified output folder.

This process may take a few minutes.

5. Visualize aligned data

The visualize tab allows you to see how the aligned meshes and the registered landmarks look like.

You can select either visualize phase 1 alignment or visualize phase 2 alignment.

Rotate or rescale the displayed meshes by moving your cursor.

6. Output

The output of each phase (1 and 2) contains four components.

Here are some examples on how to use the Auto3dgm output:

  1. Statistical shape analysis with Auto3dgm landmarks

Use the GPA module in the SlicerMorph extension on aligned_landmarks or landmarks_OSS for computing mean shape and principal component analyis. Watch this video to learn how to use the GPA module.

  1. Transformation

Use the .h5 files in rotation and scale_info folders to transfrom between the original subject space and the aligned uniformized space.