def create_labels_along_segmentation(
img: Image, labels: Sequence[Tuple[int, int]]) -> Image:
"""
Create an image with labels defined along the spinal cord segmentation (or centerline).
Input image does **not** need to be RPI (re-orientation is done within this function).
:param img: source segmentation
:param labels: list of label tuples as (z_value, label_value)
:returns: labeled segmentation (Image)
"""
og_orientation = img.orientation
if og_orientation != "RPI":
img.change_orientation("RPI")
out = zeros_like(img)
for idx_label, label in enumerate(labels):
z, value = label
# update z based on native image orientation (z should represent superior-inferior axis)
coord = Coordinate(
[z, z, z]
) # since we don't know which dimension corresponds to the superior-inferior
# axis, we put z in all dimensions (we don't care about x and y here)
_, _, z_rpi = coord.permute(img, 'RPI')
# if z=-1, replace with nz/2
if z == -1:
z_rpi = int(np.round(out.dim[2] / 2.0))
# get center of mass of segmentation at given z
x, y = ndimage.measurements.center_of_mass(
np.array(img.data[:, :, z_rpi]))
# round values to make indices
x, y = int(np.round(x)), int(np.round(y))
# display info
logger.debug(f"Label # {idx_label}: {x}, {y}. {z_rpi} --> {value}")
if len(out.data.shape) == 3:
out.data[x, y, z_rpi] = value
elif len(out.data.shape) == 2:
if z != 0:
raise ValueError(
f"2D coordinates should have a Z value of 0! Current value: {coord.z}"
)
out.data[x, y] = value
if out.orientation != og_orientation:
out.change_orientation(og_orientation)
img.change_orientation(og_orientation)
return out
def create_label_along_segmentation(self):
"""
Create an image with labels defined along the spinal cord segmentation (or centerline).
Input image does **not** need to be RPI (re-orientation is done within this function).
Example:
object_define=ProcessLabels(fname_segmentation, coordinates=[coord_1, coord_2, coord_i]), where coord_i='z,value'. If z=-1, then use z=nz/2 (i.e. center of FOV in superior-inferior direction)
Returns
"""
# reorient input image to RPI
im_rpi = self.image_input.copy().change_orientation('RPI')
im_output_rpi = zeros_like(im_rpi)
# loop across labels
for ilabel, coord in enumerate(self.coordinates):
# split coord string
list_coord = coord.split(',')
# convert to int() and assign to variable
z, value = [int(i) for i in list_coord]
# update z based on native image orientation (z should represent superior-inferior axis)
coord = Coordinate(
[z, z, z]
) # since we don't know which dimension corresponds to the superior-inferior
# axis, we put z in all dimensions (we don't care about x and y here)
_, _, z_rpi = coord.permute(self.image_input, 'RPI')
# if z=-1, replace with nz/2
if z == -1:
z_rpi = int(np.round(im_output_rpi.dim[2] / 2.0))
# get center of mass of segmentation at given z
x, y = ndimage.measurements.center_of_mass(
np.array(im_rpi.data[:, :, z_rpi]))
# round values to make indices
x, y = int(np.round(x)), int(np.round(y))
# display info
sct.printv(
'Label #' + str(ilabel) + ': ' + str(x) + ',' + str(y) + ',' +
str(z_rpi) + ' --> ' + str(value), 1)
if len(im_output_rpi.data.shape) == 3:
im_output_rpi.data[x, y, z_rpi] = value
elif len(im_output_rpi.data.shape) == 2:
assert str(
z
) == '0', "ERROR: 2D coordinates should have a Z value of 0. Z coordinate is :" + str(
z)
im_output_rpi.data[x, y] = value
# change orientation back to native
return im_output_rpi.change_orientation(self.image_input.orientation)