Update RandomLabelsToImage

@GFabien @romainVala

Now that I’ve included the ICBM template, do you think it’s a good idea to simplify a bit the example in RandomLabelsToImage? Also, Colin27 2008 is very large.

For example:

import torchio
from torchio import RandomLabelsToImage
from torchio.datasets import ICBM2009CNonlinearSymmetryc

icbm = ICBM2009CNonlinearSymmetryc(load_4d_tissues=False)

# Default Gaussian parameters
transform = RandomLabelsToImage(
    pv_label_keys=['gm', 'wm', 'csf'],
)
transformed = transform(icbm)
transformed['image'].save('/tmp/lab2im.nii')

# Specify Gaussian parameters
gaussian_parameters = {
    'gm': dict(mean=2, std=0.1),
    'wm': dict(mean=4, std=0.1),
    'csf': dict(mean=6, std=0.1),
}
transform = RandomLabelsToImage(
    pv_label_keys=['gm', 'wm', 'csf'],
    gaussian_parameters=gaussian_parameters,
)
transformed = transform(icbm)
transformed['image'].save('/tmp/lab2im.nii')

I can also add an option in the ICBM class to create a label map from the PV images.

[GFabien]

Why not, however it means that you have to specify every mean and std (even when you specify that a tissue will not be used because it will be directly taken from the original image).

[fepegar]

That would be the best solution and it would make it transparent to the transform. If this seems good to you I’ll make a PR to refactor RandomLabelsToImage to handle only single label map (be it a PV label map or not) and enable Image to take a list of paths.

Sounds good thanks! I think an if isinstance(path, list) and torch.cat(tensors) in Image.load() would do. Maybe also asserting that all affines are the same. This could be extended to Image.save() (taking a path per channel).

What I’m not sure about however is if I should make a clear distinction between PV and label maps. Indeed, you could find if you’re facing a label map by rounding the tensor and compare it to itself but with datasets such as Colin27 2008 you can be surprised. Furthermore, label maps can now be given as one hot encoded label maps so you should take the argmax to get a usual label map. Maybe the discretize attribute is enough to make this distinction clear.

I guess you’re right. At the end of the day, they are just soft or hard assignments, in 4D volumes, that can sometimes be summarized in a 3D volume. However, the Colin27 2008 is an outlier, their volume is clearly wrong (I email the maintainer, no reply).