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surf_fill.py
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surf_fill.py
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import numpy as np
import nibabel as nb
import nibabel.gifti
import scipy.ndimage
def surf_fill_vtk(vertices, polys, mat, shape):
import vtk
from vtk.util import numpy_support
voxverts = nb.affines.apply_affine(np.linalg.inv(mat), vertices)
points = vtk.vtkPoints()
points.SetNumberOfPoints(len(voxverts))
for i,pt in enumerate(voxverts):
points.InsertPoint(i, pt)
tris = vtk.vtkCellArray()
for vert in polys:
tris.InsertNextCell(len(vert))
for v in vert:
tris.InsertCellPoint(v)
pd = vtk.vtkPolyData()
pd.SetPoints(points)
pd.SetPolys(tris)
del points, tris
whiteimg = vtk.vtkImageData()
whiteimg.SetDimensions(shape)
if vtk.VTK_MAJOR_VERSION <= 5:
whiteimg.SetScalarType(vtk.VTK_UNSIGNED_CHAR)
else:
info = vtk.vtkInformation()
whiteimg.SetPointDataActiveScalarInfo(info, vtk.VTK_UNSIGNED_CHAR, 1)
ones = np.ones(np.prod(shape),dtype=np.uint8)
whiteimg.GetPointData().SetScalars(numpy_support.numpy_to_vtk(ones))
pdtis = vtk.vtkPolyDataToImageStencil()
if vtk.VTK_MAJOR_VERSION <= 5:
pdtis.SetInput(pd)
else:
pdtis.SetInputData(pd)
pdtis.SetOutputWholeExtent(whiteimg.GetExtent())
pdtis.Update()
imgstenc = vtk.vtkImageStencil()
if vtk.VTK_MAJOR_VERSION <= 5:
imgstenc.SetInput(whiteimg)
imgstenc.SetStencil(pdtis.GetOutput())
else:
imgstenc.SetInputData(whiteimg)
imgstenc.SetStencilConnection(pdtis.GetOutputPort())
imgstenc.SetBackgroundValue(0)
imgstenc.Update()
data = numpy_support.vtk_to_numpy(
imgstenc.GetOutput().GetPointData().GetScalars()).reshape(shape).transpose(2,1,0)
del pd,voxverts,whiteimg,pdtis,imgstenc
return data
def hcp_5tt(parc_file, mask_file,
lh_white, rh_white, lh_pial, rh_pial, subdiv=4):
parc = nb.load(parc_file)
mask = nb.load(mask_file)
mask_data = mask.get_data()
voxsize = np.asarray(parc.header.get_zooms()[:3])
parc_data = parc.get_data()
lh_wm = nb.gifti.read(lh_white)
rh_wm = nb.gifti.read(rh_white)
lh_gm = nb.gifti.read(lh_pial)
rh_gm = nb.gifti.read(rh_pial)
def fill_hemis(lh_surf,rh_surf):
vertices = np.vstack([lh_surf.darrays[0].data,rh_surf.darrays[0].data])
tris = np.vstack([lh_surf.darrays[1].data,
rh_surf.darrays[1].data+lh_surf.darrays[0].dims[0]])
pve_voxsize = voxsize/float(subdiv)
fill = surf_fill_vtk(vertices, tris,
parc.get_affine(), parc.shape, pve_voxsize)
pve = reduce(
lambda x,y: x+fill[0][y[0]::subdiv,y[1]::subdiv,y[2]::subdiv],
np.mgrid[:subdiv,:subdiv,:subdiv].reshape(3,-1).T,0
).astype(np.float32)
pve /= float(subdiv**3)
return pve
wm_pve = fill_hemis(lh_wm,rh_wm)
gm_pve = fill_hemis(lh_gm,rh_gm)
def group_rois(rois_ids):
m = np.zeros(parc.shape, dtype=np.bool)
for i in rois_ids:
np.logical_or(parc_data==i, m, m)
return m
gm_smooth = scipy.ndimage.gaussian_filter(
group_rois([8,47,17,18,53,54]).astype(np.float32),
sigma=voxsize)
subcort_smooth = scipy.ndimage.gaussian_filter(
group_rois([10,11,12,13,26,49,50,51,52,58]).astype(np.float32),
sigma=voxsize)
wm_smooth = scipy.ndimage.gaussian_filter(
group_rois([7,16,28,46,60,85,192,
250,251,252,253,254,255]).astype(np.float32),
sigma=voxsize)
# remove csf at the end of brainstem for streamlines to medulla
# suppose hcp orientation storage
bs = (parc_data==16).any(0).any(0)
lbs = np.where(bs)[0][0]+3
outer_csf = np.logical_and(mask_data>0, parc_data==0)
outer_csf[...,:lbs] = 0
csf_smooth = scipy.ndimage.gaussian_filter(
np.logical_or(
group_rois([4,5,14,15,24,30,31,43,44,62,63,72]),
outer_csf).astype(np.float32),
sigma=voxsize)
csf_smooth[...,:lbs] = 0
wm = wm_pve+wm_smooth-csf_smooth-subcort_smooth
wm[wm>1] = 1
wm[wm<0] = 0
gm = gm_pve-wm_pve-wm-subcort_smooth+gm_smooth
gm[gm<0] = 0
tt5 = np.concatenate([gm[...,np.newaxis],
subcort_smooth[...,np.newaxis],
wm[...,np.newaxis],
csf_smooth[...,np.newaxis],
np.zeros(parc.shape+(1,),dtype=np.float32)],3)
tt5[...,:lbs,:] = 0
tt5/=tt5.sum(-1)[...,np.newaxis]
tt5[np.isnan(tt5)]=0
return nb.Nifti1Image(tt5.astype(np.float32),parc.get_affine())
def read_surf(fname, surf_ref):
if fname[-4:] == '.gii':
gii = nb.gifti.read(fname)
return gii.darrays[0].data, gii.darrays[1].data
else:
verts,tris = nb.freesurfer.read_geometry(fname)
ras2vox = np.array([[-1,0,0,128],[0,0,-1,128],[0,1,0,128],[0,0,0,1]])
surf2world = surf_ref.get_affine().dot(ras2vox)
verts[:] = nb.affines.apply_affine(surf2world, verts)
return verts, tris
def make_5tt(parc_file, lh_white, rh_white, lh_pial, rh_pial, subdiv=4):
parc = nb.load(parc_file)
voxsize = np.asarray(parc.header.get_zooms()[:3])
parc_data = parc.get_data()
lh_wm = read_surf(lh_white, parc)
rh_wm = read_surf(rh_white, parc)
lh_gm = read_surf(lh_pial, parc)
rh_gm = read_surf(rh_pial, parc)
def fill_hemis(lh_surf,rh_surf):
vertices = np.vstack([lh_surf[0],rh_surf[0]])
tris = np.vstack([lh_surf[1],
rh_surf[1]+lh_surf[0].shape[0]])
pve_voxsize = voxsize/float(subdiv)
mat = parc.affine.dot(np.diag([1/float(subdiv)]*3+[1]))
shape = np.asarray(parc.shape)*subdiv
fill = surf_fill_vtk(vertices, tris, mat, shape)
pve = reduce(
lambda x,y: x+fill[y[0]::subdiv,y[1]::subdiv,y[2]::subdiv],
np.mgrid[:subdiv,:subdiv,:subdiv].reshape(3,-1).T,0
).astype(np.float32)
pve /= float(subdiv**3)
return pve
wm_pve = fill_hemis(lh_wm,rh_wm)
gm_pve = fill_hemis(lh_gm,rh_gm)
def group_rois(rois_ids):
m = np.zeros(parc.shape, dtype=np.bool)
for i in rois_ids:
np.logical_or(parc_data==i, m, m)
return m
gm_rois = group_rois([8,47,17,18,53,54]).astype(np.float32)
gm_smooth = scipy.ndimage.gaussian_filter(gm_rois, sigma=voxsize)
subcort_rois = group_rois([10,11,12,13,26,49,50,51,52,58]).astype(np.float32)
subcort_smooth = scipy.ndimage.gaussian_filter(subcort_rois, sigma=voxsize)
wm_rois = group_rois([7,16,28,46,60,85,192,88,
250,251,252,253,254,255]).astype(np.float32)
wm_smooth = scipy.ndimage.gaussian_filter(wm_rois,sigma=voxsize)
# remove csf at the end of brainstem for streamlines to medulla
# suppose hcp orientation storage
bs_mask = parc_data==16
bs_vdc_dil = scipy.ndimage.morphology.binary_dilation(group_rois([16,60,28]), iterations=2)
# mask of boundary between brainstem and cerebellar gray matter
bs_vdc_excl = np.logical_and(bs_vdc_dil, np.logical_not(group_rois([16,7,46,60,28,10,49,2,41,0])))
lbs = np.where((bs_mask).any(-1).any(0))[0][-1]-3
parc_data_mask = parc_data>0
outer_csf = np.logical_and(
np.logical_not(parc_data_mask),
scipy.ndimage.morphology.binary_dilation(parc_data_mask))
## create a fake GM rois at the end of brainstem for cerebro-spinal tracking
nb.save(nb.Nifti1Image(outer_csf.astype(np.int32),parc.affine),'outer_csf.nii')
csf_rois = group_rois([4,5,14,15,24,30,31,43,44,62,63,72])
nb.save(nb.Nifti1Image(csf_rois.astype(np.int32),parc.affine),'csf_rois.nii')
csf_smooth = scipy.ndimage.gaussian_filter(
np.logical_or(csf_rois, outer_csf).astype(np.float32),
sigma=voxsize)
nb.save(nb.Nifti1Image(csf_smooth,parc.affine),'csf_smooth.nii')
bs_roi = csf_smooth.copy()
bs_roi[...,:lbs,:] = 0
csf_smooth[...,lbs:,:] = 0
wm_smooth[...,lbs:,:] = 0
# add csf around brainstem and ventral DC to remove direct connection to gray matter
csf_smooth[bs_vdc_excl] += gm_smooth[bs_vdc_excl]
gm_smooth[bs_vdc_excl] = 0
mask88 = parc_data==88
print csf_smooth[mask88].sum(), subcort_smooth[mask88].sum()
# csf_smooth -= wm_smooth
# csf_smooth[csf_smooth<0]=0
nb.save(nb.Nifti1Image(wm_pve,parc.affine),'wm_pve.nii')
nb.save(nb.Nifti1Image(wm_smooth,parc.affine),'wm_smooth.nii')
nb.save(nb.Nifti1Image(subcort_smooth,parc.affine),'subcort_smooth.nii')
wm = wm_pve+wm_smooth-csf_smooth-subcort_smooth
wm[wm>1] = 1
wm[wm<0] = 0
print 267, np.count_nonzero(wm[mask88])
gm = gm_pve-wm_pve-wm-subcort_smooth+gm_smooth+bs_roi
gm[gm<0] = 0
tt5 = np.concatenate([gm[...,np.newaxis],
subcort_smooth[...,np.newaxis],
wm[...,np.newaxis],
csf_smooth[...,np.newaxis],
np.zeros(parc.shape+(1,),dtype=np.float32)],3)
tt5/=tt5.sum(-1)[...,np.newaxis]
tt5[np.isnan(tt5)]=0
return nb.Nifti1Image(tt5.astype(np.float32),parc.affine)