Hello,

The problem that I am trying to solve with SimpleITK:

1/ I have a temporal sequence of volumes from the same patient

2/ I have one segmentation mask of the organ under observation.

3/ I want to get segmentations based on registration and transforms from the one mask for all the temporal sequences:

4/ Apply that for meshes

I first obtain the deformation fields and then warp the mask with the displacement field for all the volumes in the temporal sequence. Therefore I gain the segmentations for all volumes.

```
Vref = sitk.ReadImage(original_mask)
dvf_sitk = sitk.ReadImage(deformation_field_path)
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(Vref)
dis_tx = sitk.DisplacementFieldTransform(sitk.Cast(dvf_sitk,sitk.sitkVectorFloat64))
resampler.SetTransform(dis_tx)
out = resampler.Execute(Vref)
```

However, I want to work with meshes. For the first segmented volume I applied Delaunay surface triangulation to obtain the mesh. Then, Iâ€™d like to change the mesh based on the deformation field (therefore not changing the adjacency matrix and cardinality of the set of nodes).

I thought about using the displacementfield, however they yield float values.

```
np.unique(sitk.GetArrayFromImage(dvf_sitk))
array([-33.68408 , -33.51517 , -33.3977 , ..., 19.781471, 19.782059, 19.919436], dtype=float32)
```

The documentation for ResampleImageFilter / WarpImageFilter states:

Note that the choice of interpolator function can be important. This function is set via SetInterpolator() . The default is LinearInterpolateImageFunction <InputImageType, TInterpolatorPrecisionType>, which is reasonable for ordinary medical images. However, some synthetic images have pixels drawn from a finite prescribed set.

So, the warping after using the values from displacementfield interpolates the float numbers into integers. Since I want to work with mesh, how can I get the mappings how each point changed?

For instance, voxel [3,5,7] after resampling can be found at [3,5,8] etc.

Thanks!