b723d15de620cd2a3a4dbd71e491b59d

B-spline deformation.ipynb

notebook.ipynb

Do you mean for visualization or to apply elastic deformations to 3D images?

For the former, I'd user 3D Slicer and VTK.
For the latter, TorchIO: https://torchio.readthedocs.io/transforms/augmentation.html#randomelasticdeformation

I mean, to apply 3D deformation to 3d images (e.g., CT scans). I am a radiologist working on deep learning in kidney tumours. I have been looking for efficient spline-based deformations of the tumour as data-augmentation. People have developed elaborate elastic / deep-learning based pipeline but afaik a simple solution with splines would be quicker and logical. I haven't found an implementation yet.

You can use TorchIO, as I mentioned above.

Hi, thanks for this.

How possible is it to apply deformations only around a label/mask in a subject using the randomelacticdeformation method?
Example:

mri = tio.ScalarImage('')
label = tio.LabelMap('')

subject = tio.Subject(mri=sample, seg=label)
subject.plot()

transform = tio.RandomElasticDeformation()
new_image = transform(subject)

Hi, thanks for this.

How possible is it to apply deformations only around a label/mask in a subject using the randomelacticdeformation method? Example:

mri = tio.ScalarImage('')
label = tio.LabelMap('')

subject = tio.Subject(mri=sample, seg=label)
subject.plot()

transform = tio.RandomElasticDeformation()
new_image = transform(subject)

Interesting! Here's an attempt, not perfect but might be good enough for your purposes:

import SimpleITK as sitk
import torch
import torchio as tio

size = 256
grid_spacing = 15

grid = sitk.GridSource(
    outputPixelType=sitk.sitkFloat32,
    size=(size, size, size),
    sigma=(0.5, 0.5, 0.5),
    gridSpacing=(grid_spacing, grid_spacing, grid_spacing),
    gridOffset=(0, 0, 0),
    spacing=(1, 1, 1),
) * -1

image = tio.ScalarImage.from_sitk(grid)

mask_tensor = torch.zeros_like(image.data, dtype=torch.bool)
mask_tensor[:, 50:150, 30:180, 50:200] = True
mask = tio.LabelMap(tensor=mask_tensor, affine=image.affine)

crop = tio.CropOrPad((100, 150, 150), mask_name="mask")
subject = tio.Subject(
    image=image,
    mask=mask,
)
roi = crop(subject)

elastic = tio.RandomElasticDeformation(
    locked_borders=2,
    max_displacement=20,
)

torch.manual_seed(0)
deformed = elastic(roi.image)

resample = tio.Resample(target=image)
resampled = resample(deformed)

result_tensor = image.data
result_tensor[mask_tensor] = resampled.data[mask_tensor]
result = tio.ScalarImage(tensor=result_tensor, affine=image.affine)
result.plot(figsize=(9, 3))