DeepSSM Image-based Registration Problems

[iyerkrithika21]

There are few issues to the image-based registration (because we are assuming we don't have access to ground truth surfaces during inference) process followed for evaluation:

1. Function: get_image_registration_transform

• Translation transform crashes here: itk_transform = SimpleITK.TranslationTransform()
• The code should be changed to:

    # Get transform matrix
    parameter_map = result_transform_parameters.GetParameterMap(0)
    transform_params = np.array(parameter_map['TransformParameters'], dtype=float)
    if transform_type == 'rigid':
        itk_transform = SimpleITK.Euler3DTransform()
    elif transform_type == 'similarity':
        itk_transform = SimpleITK.Similarity3DTransform()
    elif transform_type!= 'tranlation':
        print("Error: " + transform_type + " transform unimplemented.")
    itk_transform.SetParameters(transform_params)
    itk_transform_matrix = np.eye(4)
    itk_transform_matrix[:3,:3] = np.array(itk_transform.GetMatrix()).reshape(3,3)
    if transform_type != 'translation':
        itk_transform_matrix[-1,:3] = np.array(itk_transform.GetTranslation())
   else:
       itk_transform_matrix[-1,:3] = transform_params.T

2. Should we expose other parameters for image registration to the users? This registration might require some parameter tuning for other complex anatomies.

3. 'rigid' is hardcoded here, so it always applies 'rigid' transformation even when others transform types are passed.

4. As per ITK documentation:

• Rigid (itk::Euler3DTransform) applies a rotation and translation to the space given 3 euler angles and a 3D translation. Rotation is about a user specified center.
• Similarity (itk::Similarity3DTransform) applies a rotation, translation and isotropic scaling to the space So do we need to call all three transforms as done in 'groom_val_test_images()' Lines: 396, 409, 422 ?