def _run_interface(self, runtime):
if not isdefined(self.inputs.output_file):
self.inputs.output_file = self._gen_output(self.inputs.in_file,
self._suffix)
print self.inputs.in_file
print self.inputs.reference
header = self.inputs.header
pet = volumeFromFile(self.inputs.in_file)
reference = volumeFromFile(self.inputs.reference)
out = volumeLikeFile(self.inputs.reference, self.inputs.output_file)
ndim = len(pet.data.shape)
vol = pet.data
if ndim > 3:
try:
time_frames = [
float(s) for s, e in header['Time']["FrameTimes"]["Values"]
]
except ValueError:
time_frames = [1.]
vol = simps(pet.data, time_frames, axis=4)
idx = reference.data > 0
ref = np.mean(vol[idx])
print "SUVR Reference = ", ref
vol = vol / ref
out.data = vol
out.writeFile()
out.closeVolume()
return runtime
def _run_interface(self, runtime):
header = json.load(open(self.inputs.header, "r"))
if not isdefined(self.inputs.out_file):
self.inputs.out_file = self._gen_output(self.inputs.in_file,
self._suffix)
try:
dose = float(header["RadioChem"]["InjectedRadioactivity"])
except ValueError:
print(
"Error: Could not find injected dose in subject's header. Make sure subject has a .json header in BIDS format with [RadioChem][InjectedRadioactivity]"
)
exit(1)
try:
weight = float(header["Info"]["BodyWeight"])
except ValueError:
print(
"Error: Could not find subject's body weight in header. Make sure subject has a .json header in BIDS format with [Info][BodyWeight]"
)
exit(1)
pet = volumeFromFile(self.inputs.in_file)
reference = volumeFromFile(self.inputs.reference)
out = volumeLikeFile(self.inputs.reference, self.inputs.out_file)
ndim = len(pet.data.shape)
vol = pet.data
if ndim > 3:
dims = pet.getDimensionNames()
i = dims.index('time')
if not isdefined(self.inputs.start_time):
start_time = 0
else:
start_time = self.inputs.start_time
if not isdefined(self.inputs.end_time):
end_time = header['Time']['FrameTimes']['Values'][-1][1]
else:
end_time = self.inputs.end_time
#time_frames = time_indices = []
#for i in range(vol.shape[i]) :
# s=header['Time']["FrameTimes"]["Values"][i][0]
# e=header['Time']["FrameTimes"]["Values"][i][1]
# print(i)
# if s >= start_time and e <= end_time :
# time_indices.append(i)
# time_frames.append(float(e) - float(s) )
#print(time_indices)
#print(time_frames)
time_frames = [
float(s) for s, e in header['Time']["FrameTimes"]["Values"]
]
vol = simps(pet.data, time_frames, axis=i)
vol = vol / (dose / weight)
out.data = vol
out.writeFile()
out.closeVolume()
return runtime
def mnc_labeling(g, CT_file, output_file, multiplier=1.0):
#labeled_ct = f.volumeFromFile(CT_file)
labeled_ct = f.volumeLikeFile(CT_file, output_file)
#labeled_ct.openFile()
size = labeled_ct.getSizes()
print "CT size is ", size
print "first and last voxel intensities are:", labeled_ct.data[0, 0, 0]
print labeled_ct.data[size[0] - 1, size[1] - 1, size[2] - 1]
spacing = labeled_ct.getSeparations()
start = labeled_ct.getStarts()
#set the volume to 0
for i in range(size[0]):
for j in range(size[1]):
for k in range(size[2]):
labeled_ct.data[i, j, k] = 0
#set the labels
for e in g.edge_list():
l = g.edge_property(e, 'label')
vlist = [e[0]] + [e[1]] + g.edge_property(e, 'intermediaries')
for v in vlist:
w = g.vertices[v].centre
r = multiplier * g.vertices[v].radius
wtovfloat = labeled_ct.convertWorldToVoxel(w)
wtov = np.array(
[int(wtovfloat[0]),
int(wtovfloat[1]),
int(wtovfloat[2])])
if wtov[0] < size[0] and wtov[1] < size[1] and wtov[2] < size[
2] and wtov[0] > -1 and wtov[1] > -1 and wtov[2] > -1:
labeled_ct.data[int(wtov[0]), int(wtov[1]), int(wtov[2])] = l
for i in range(max(0, int(wtov[0] - (r / spacing[0]))),
min(int(wtov[0] + (r / spacing[0])), size[0])):
for j in range(max(0, int(wtov[1] - (r / spacing[1]))),
min(int(wtov[1] + (r / spacing[1])), size[1])):
for k in range(
max(0, int(wtov[2] - (r / spacing[2]))),
min(int(wtov[2] + (r / spacing[2])), size[2])):
currw = labeled_ct.convertWorldToVoxel(
np.array([i, j, k]))
if calc_dist(w[0] - currw[0], w[1] - currw[1],
w[2] - currw[2]) < r:
labeled_ct.data[int(i), int(j), int(k)] = l
labeled_ct.writeFile()
labeled_ct.closeVolume()
return 0
def _run_interface(self, runtime):
if not isdefined(self.inputs.out_file):
self.inputs.out_file = self._gen_output(self.inputs.in_file,
self._suffix)
header = json.load(open(self.inputs.header, "r"))
pet = volumeFromFile(self.inputs.in_file)
reference = volumeFromFile(self.inputs.reference)
out = volumeLikeFile(self.inputs.reference, self.inputs.out_file)
ndim = len(pet.data.shape)
vol = pet.data
if ndim > 3:
dims = pet.getDimensionNames()
i = dims.index('time')
if not isdefined(self.inputs.start_time):
start_time = 0
else:
start_time = self.inputs.start_time
if not isdefined(self.inputs.end_time):
end_time = header['Time']['FrameTimes']['Values'][-1][1]
else:
end_time = self.inputs.end_time
try:
#time_frames = [ float(s) for s,e in header['Time']["FrameTimes"]["Values"] ]
time_frames = [
float(s) for s, e in header['Time']["FrameTimes"]["Values"]
if s >= start_time and e <= end_time
]
except ValueError:
time_frames = [1.]
vol = simps(pet.data, time_frames, axis=i)
idx = reference.data > 0
ref = np.mean(vol[idx])
print "SUVR Reference = ", ref
vol = vol / ref
out.data = vol
out.writeFile()
out.closeVolume()
return runtime
def do_component(component_nr):
input_volume = volumeFromFile(
'/scratch/intersection_output_disjoint/input_%d.mnc' % component_nr)
output_volume = volumeLikeFile(
'/scratch/intersection_output_disjoint/input_%d.mnc' % component_nr,
'/scratch/final_smoothed_disjoint_components/component_%d.mnc' %
component_nr,
dtype='ubyte')
for j in range(input_volume.data.shape[1]):
print component_nr, j
output_volume.data[:, j, :] = blerp(input_volume.data[:, j, :])
output_volume.writeFile()
output_volume.closeVolume()
input_volume.closeVolume()
"""
def predict(model_name,input_path,input_filenames,output_path,output_filename,stride=(2,1,1)):
#model = load_model_own(model_name,model_version=model_version)
model = load_model(model_name)
(_,x,y,z,d) = model.input.shape.as_list()
patchsize = (x,y,z)
l = []
for n in range(0,d):
mncfile = os.path.join(input_path,input_filenames[n])
img = load_minc_as_np(mncfile)
patches = get_patches(img,patchsize,stride)
l.append(patches)
patches = np.concatenate(l,axis=4)
out_vol = pyminc.volumeLikeFile(os.path.join(input_path,input_filenames[0]),os.path.join(output_path,output_filename))
containersize= out_vol.data.shape
predicted_vol = model_predict_from_patches(model,patches,containersize,stride=stride)
out_vol.data = predicted_vol
out_vol.writeFile()
out_vol.closeVolume()
print('Done predicting: ' + os.path.join(output_path,output_filename))
try: RTSS = dicom.read_file(args.RTX) print RTSS.StructureSetROISequence[0].ROIName ROIs = RTSS.ROIContourSequence if args.verbose: print "Found",len(ROIs),"ROIs" volume = pyminc.volumeFromFile(args.MINC) for ROI_id,ROI in enumerate(ROIs): # Create one MNC output file per ROI RTMINC_outname = args.RTMINC if len(ROIs) == 1 else args.RTMINC[:-4] + "_" + str(ROI_id) + ".mnc" RTMINC = pyminc.volumeLikeFile(args.MINC,RTMINC_outname) contour_sequences = ROI.ContourSequence if args.verbose: print " --> Found",len(contour_sequences),"contour sequences for ROI:",RTSS.StructureSetROISequence[ROI_id].ROIName for contour in contour_sequences: assert contour.ContourGeometricType == "CLOSED_PLANAR" current_slice_i_print = 0 if args.verbose: print "\t",contour.ContourNumber,"contains",contour.NumberOfContourPoints world_coordinate_points = np.array(contour.ContourData) world_coordinate_points = world_coordinate_points.reshape((contour.NumberOfContourPoints,3))
def pyrtx2mnc():
#Read dicom rt structure file with pydicom
RTSS = dicom.read_file(args.RTX)
if args.verbose:
print "Regions found: {}".format(
RTSS.StructureSetROISequence[0].ROIName)
#Fetch the minc volume from the input
volume = pyminc.volumeFromFile(args.MINC)
#For every ROI encountered create a seperate mapping and file.
for ROI_id, ROI in enumerate(RTSS.ROIContourSequence):
# Create one MNC output file per ROI
RTMINC_outname = args.RTMINC if len(
RTSS.ROIContourSequence
) == 1 else args.RTMINC[:-4] + "_" + str(ROI_id) + ".mnc"
#Create volume like the minc file and name it RTMINC_outname
RTMINC = pyminc.volumeLikeFile(args.MINC, RTMINC_outname)
contour_sequences = ROI.ContourSequence
if args.verbose:
print " --> Found", len(
contour_sequences
), "contour sequences for ROI:", RTSS.StructureSetROISequence[
ROI_id].ROIName
#For each contoursequence convert world coordinates, draw the contour, check if the drawn points are inside a voxel center grid
for contour in contour_sequences:
#Raise error if we do not have a CLOSED_PLANAR type.
assert contour.ContourGeometricType == "CLOSED_PLANAR"
if args.verbose:
print "\t", contour.ContourNumber, "contains", contour.NumberOfContourPoints
#Convert contourdata to numpy array and reshape as xyz coloumns
world_coordinate_points = np.array(contour.ContourData).reshape(
(contour.NumberOfContourPoints, 3))
#Create empty 2d slice(array) with the original slice dimensions
current_slice = np.zeros(
(volume.getSizes()[1], volume.getSizes()[2]))
current_slice_inner = np.zeros(
(volume.getSizes()[1], volume.getSizes()[2]), dtype=np.float)
#Create empty 2d array of size of the x,y coordinates
voxel_coordinates_inplane = np.zeros(
(len(world_coordinate_points), 2))
current_slice_i = 0
#Convert world to voxel and set current_slice to the z space or slice number add x,y coordinates to the voxel_coordinates_inplane
for wi, world in enumerate(world_coordinate_points):
voxel = volume.convertWorldToVoxel(
[-world[0], -world[1], world[2]])
current_slice_i = voxel[0]
voxel_coordinates_inplane[wi, :] = [voxel[2], voxel[1]]
#Round x,y coordinates and convert to array again
converted_voxel_coordinates_inplane = np.array(
np.round(voxel_coordinates_inplane), np.int32)
#Fill the contour place result in image current_slice_inner array
cv2.fillPoly(current_slice_inner,
pts=[converted_voxel_coordinates_inplane],
color=1)
#only return the two tuples that are not zero convert to array and transpose them
points = np.array(np.nonzero(current_slice_inner)).T
#Path converts the inplane voxels to a path class with callable values such as contains_points and more(https://matplotlib.org/api/path_api.html)
p = Path(voxel_coordinates_inplane)
#Create np.array of True or false values if the points are in the path
grid = p.contains_points(points[:, [1, 0]])
for pi, point in enumerate(points):
if not grid[pi]:
# REMOVE EDGE POINT BECAUSE CENTER IS NOT INCLUDED
current_slice_inner[point[0], point[1]] = 0
#Only change the data structure inside the minc volume created earlier
RTMINC.data[np.int(current_slice_i)] += current_slice_inner
# Remove even areas - implies a hole.
RTMINC.data[RTMINC.data % 2 == 0] = 0
#Write and close file
RTMINC.writeFile()
RTMINC.closeVolume()
#Copy the name of the RTstruct (defined in Mirada) to the name of the MNC file
if args.verbose:
print 'minc_modify_header -sinsert dicom_0x0008:el_0x103e="' + RTSS.StructureSetROISequence[
ROI_id].ROIName + '" ' + RTMINC_outname
os.system('minc_modify_header -sinsert dicom_0x0008:el_0x103e="' +
RTSS.StructureSetROISequence[ROI_id].ROIName + '" ' +
RTMINC_outname)
volume.closeVolume()
