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Created September 21, 2022 18:25
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# Convert volume nodes of 3D Slicer to/from xarray and read/write into zarr format
def xarrayFromVolume(volumeNode) -> "xr.DataArray":
"""Convert an volume node to an xarray.DataArray.
Origin and spacing metadata is preserved in the xarray's coords. The
Direction is set in the `direction` attribute.
Dims are labeled as `x`, `y`, `z`, `t`, and `c`.
This interface is and behavior is experimental and is subject to possible
future changes."""
import xarray as xr
import numpy as np
array_view = slicer.util.arrayFromVolume(volumeNode)
l_spacing = volumeNode.GetSpacing()
l_origin = volumeNode.GetOrigin()
l_size = volumeNode.GetImageData().GetDimensions()
image_dimension = 3
image_dims = ("x", "y", "z", "t")
coords = {}
for l_index, dim in enumerate(image_dims[:image_dimension]):
coords[dim] = np.linspace(
l_origin[l_index] + (l_size[l_index] - 1) * l_spacing[l_index],
dims = list(reversed(image_dims[:image_dimension]))
components = volumeNode.GetImageData().GetNumberOfScalarComponents()
if components > 1:
coords["c"] = np.arange(components, dtype=np.uint32)
directionMatrixVtk = vtk.vtkMatrix4x4()
direction = np.flip(slicer.util.arrayFromVTKMatrix(directionMatrixVtk))
attrs = {"direction": direction}
for attributeName in volumeNode.GetAttributeNames():
attrs[key] = volumeNode.GetAttribute(attributeName)
data_array = xr.DataArray(array_view, dims=dims, coords=coords, attrs=attrs)
return data_array
def volumeFromXarray(data_array: "xr.DataArray"):
"""Convert an xarray.DataArray to a MRML volume node.
import numpy as np
import builtins
if not {"t", "z", "y", "x", "c"}.issuperset(data_array.dims):
raise ValueError('Unsupported dims, supported dims: "t", "z", "y", "x", "c".')
image_dims = list({"t", "z", "y", "x"}.intersection(set(data_array.dims)))
image_dimension = len(image_dims)
ordered_dims = ("t", "z", "y", "x")[-image_dimension:]
is_vector = "c" in data_array.dims
if is_vector:
ordered_dims = ordered_dims + ("c",)
values = data_array.values
if ordered_dims != data_array.dims:
dest = list(builtins.range(len(ordered_dims)))
source = dest.copy()
for ii in builtins.range(len(ordered_dims)):
source[ii] = data_array.dims.index(ordered_dims[ii])
values = np.moveaxis(values, source, dest).copy()
volumeNode = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLScalarVolumeNode' if not is_vector else 'vtkMRMLVectorVolumeNode')
slicer.util.updateVolumeFromArray(volumeNode, values) # is_vector)
l_origin = [0.0] * image_dimension
l_spacing = [1.0] * image_dimension
for l_index, dim in enumerate(image_dims):
coords = data_array.coords[dim]
if coords.shape[0] > 1:
l_origin[l_index] = float(coords[0])
l_spacing[l_index] = float(coords[1]) - float(coords[0])
if "direction" in data_array.attrs:
direction = data_array.attrs["direction"]
directionMatrixVtk = slicer.util.vtkMatrixFromArray(np.flip(direction))
ignore_keys = set(["direction", "origin", "spacing"])
for key in data_array.attrs:
if not key in ignore_keys:
volumeNode.SetAttribute(key, data_array.attrs[key])
return volumeNode
# Convert to xarray
import SampleData
sampleDataLogic = SampleData.SampleDataLogic()
volumeNode = sampleDataLogic.downloadMRBrainTumor1()
da = xarrayFromVolume(volumeNode)
# Save as zarr
ds = da.to_dataset(name='image').chunk({'x': 20, 'y': -1})
zs = ds.to_zarr('c:/tmp/zarrimage/', mode='w')
# Load from zarr
import xarray as xr
ds=xr.open_dataset(r'c:\tmp\zarrimage', engine='zarr')
# Convert to volume
volumeNode = volumeFromXarray(da)
# Display volume
# Load small subset
ds=xr.open_dataset(r'c:\tmp\zarrimage', engine='zarr', chunks={})
#test = ds.sel(x=np.arange(-20, 20), method='nearest')
#volumeNodePart = volumeFromXarray(test.image)
# Dynamic update
updateVolumeFromArray(volumeNode, ds.sel(x=np.arange(250, 290), method='nearest').image)
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