Hi,
I'm working on a new registration method, and decided to use learn2reg_nlst_paired_lung_ct.ipynb as a base.
However, the conversion from keypoints from voxel coordinates to warping coordinates in the range [-1, 1] seems incorrect to me.
It is done like this (in the forward function):
offset = torch.as_tensor(fixed_image.shape[-3:]).to(fixed_keypoints.device) / 2
offset = offset[None][None]
ddf_keypoints = torch.flip((fixed_keypoints - offset) / offset, (-1,))
But in the Warp class of Monai, the conversion is done like this:
for i, dim in enumerate(grid.shape[1:-1]):
grid[..., i] = grid[..., i] * 2 / (dim - 1) - 1
index_ordering: list[int] = list(range(spatial_dims - 1, -1, -1))
grid = grid[..., index_ordering] # z, y, x -> x, y, z
Which seems correct to me, since voxel coordinates should max out at dim - 1 for indexing.
The two are almost equivalent, except that in the notebook, image.shape is used and not image.shape - 1, so I think the notebook is wrong.
Feel free to correct me if I'm wrong, because I'm lacking info on what exactly are those keypoints and how they were obtained.
Hi,
I'm working on a new registration method, and decided to use learn2reg_nlst_paired_lung_ct.ipynb as a base.
However, the conversion from keypoints from voxel coordinates to warping coordinates in the range [-1, 1] seems incorrect to me.
It is done like this (in the forward function):
But in the Warp class of Monai, the conversion is done like this:
Which seems correct to me, since voxel coordinates should max out at dim - 1 for indexing.
The two are almost equivalent, except that in the notebook, image.shape is used and not image.shape - 1, so I think the notebook is wrong.
Feel free to correct me if I'm wrong, because I'm lacking info on what exactly are those keypoints and how they were obtained.