def getaveragedicescore2d(moving,fixed,parameter_file):
#Calculates the average dice score per slice of registration
#between all combinations of moving and fixed images
#moving = array with patientnumbers of moving images
#fixed = array with patientnumbers of fixed images
DCscores = [0]*nrslices
n = 0
for i in fixed:
for j in moving:
if i == j: continue
register3d(i,j,parameter_file,runnr=runnr,verbose=False)
transform3d(j,runnr,transformmask=True)
transformed = findtransformedmask(runnr)
fixed = findfixedmask3d(i)
for k in range(nrslices):
if sum(sum(x) for x in transformed[k])+sum(sum(x) for x in fixed[k]) == 0:
DCscores[k] += 1
else:
DCscores[k] += medpyDC(transformed[k],fixed[k])
n += 1
for i in range(nrslices):
DCscores[i] = DCscores[i]/n
output_dir = f'results{runnr}'
shutil.rmtree(output_dir)
return DCscores
def getaveragedicescore3d(moving,fixed,parameter_file):
#Calculates the average dice score of registration
#between all combinations of moving and fixed images
#moving = array with patientnumbers of moving images
#fixed = array with patientnumbers of fixed images
DCscore = 0
n = 0
for i in fixed:
for j in moving:
if i == j: continue
register3d(i,j,parameter_file,runnr=runnr,verbose=False)
transform3d(j,runnr,transformmask=True)
transformed = findtransformedmask(runnr)
fixed = findfixedmask3d(i)
DCscore += medpyDC(transformed,fixed)
n += 1
DCscore /= n
output_dir = f'results{runnr}'
shutil.rmtree(output_dir)
return DCscore
def getfinalmasksMI(atlasset,unknownset,parameter_file,threshold1=0.5,threshold2=0.5,modelnr=None):
#if modelnr != None, all transformed masks will be read/written to a transformedmasksmodel folder.
if modelnr != None:
transformed_dir = f'transformedmasksmodel{modelnr}'
if os.path.exists(transformed_dir) is False:
os.mkdir(transformed_dir)
fixed = unknownset
moving = atlasset
finalmasks = []
for i in fixed:
transformedmasks = []
MIscores = []
for j in moving:
if modelnr != None:
try:
mask_path = os.path.join(transformed_dir,"IM"+str(j)+"IF"+str(i)+"mask.mhd")
transformedmask = sitk.ReadImage(mask_path)
transformedmask_array = sitk.GetArrayFromImage(transformedmask)
transformedmasks.append(transformedmask_array)
with open(os.path.join(transformed_dir,"MIscore"+"IM"+str(j)+"IF"+str(i)+".txt"), 'r') as f:
MIscores.append(float(f.readlines()[0]))
continue
except Exception:
pass
register3d(i,j,parameter_file,runnr=runnr,verbose=False)
transform3d(j,runnr,transformmask=True)
transformedmasks.append(findtransformedmask(runnr))
MIscores.append(readMI(runnr))
if modelnr != None:
mask_path = os.path.join(transformed_dir,"IM"+str(j)+"IF"+str(i)+"mask.mhd")
savemask(findtransformedmask(runnr), mask_path, i)
with open(os.path.join(transformed_dir,"MIscore"+"IM"+str(j)+"IF"+str(i)+".txt"), 'w') as f:
f.writelines(str(readMI(runnr)))
finalmasks.append(votingbased_MIweighted_3d_all(transformedmasks, MIscores, threshold1, threshold2))
output_dir = f'results{runnr}'
if os.path.exists(output_dir): shutil.rmtree(output_dir)
return finalmasks
def getfinalmasks2dDice(atlasset,unknownset,DCscores2D,parameter_file, threshold1=0.5, threshold2=0.5, modelnr=None):
#This function performs step 6-8 + 10 of the
#plan, using dice scores for each slice in the atlas images.
#if modelnr != None, all transformed masks will be read/written to a transformedmasksmodel folder.
if modelnr != None:
transformed_dir = f'transformedmasksmodel{modelnr}'
if os.path.exists(transformed_dir) is False:
os.mkdir(transformed_dir)
fixed = unknownset
moving = atlasset
finalmasks = []
for i in fixed:
transformedmasks = []
for j in moving:
if modelnr != None:
try:
mask_path = os.path.join(transformed_dir,"IM"+str(j)+"IF"+str(i)+"mask.mhd")
transformedmask = sitk.ReadImage(mask_path)
transformedmask_array = sitk.GetArrayFromImage(transformedmask)
transformedmasks.append(transformedmask_array)
continue
except Exception:
pass
register3d(i,j,parameter_file,runnr=runnr,verbose=False)
transform3d(j,runnr,transformmask=True)
transformedmasks.append(findtransformedmask(runnr))
if modelnr != None:
mask_path = os.path.join(transformed_dir,"IM"+str(j)+"IF"+str(i)+"mask.mhd")
savemask(findtransformedmask(runnr), mask_path, i)
finalmasks.append(votingbased_DCweighted_3d_slice(transformedmasks, DCscores2D, threshold1, threshold2))
output_dir = f'results{runnr}'
if os.path.exists(output_dir): shutil.rmtree(output_dir)
return finalmasks
#runnr = register2d(fixednr,movingnr,slicenr,parameter_file)
runnr = register3d(fixednr, movingnr, parameter_file1)
#Before this step: change offset in parameterfile from fixed to moving image!
#runnr = 20
#transform command
#transform2d(movingnr,slicenr,runnr,transformmask=True)
transform3d(movingnr, runnr, transformmask=True)
#visualisation commands
#visualize2d(fixednr,movingnr,slicenr,runnr)
#visualize3d(fixednr,movingnr,slicenr,runnr)
fixedmask = findfixedmask(107)
transformedmask = findtransformedmask(runnr)
print(medpyDC(transformedmask, fixedmask))
print(medpyHD(transformedmask, fixedmask))
fixed_image_path = os.path.join(f"results{runnr}", "result.mhd")
moving_image_path = os.path.join(data_path, f"p{movingnr}\mr_bffe.mhd")
output_dir = f'results{runnr}'
el = elastix.ElastixInterface(elastix_path=elastix_path)
#run model
el.register(fixed_image=fixed_image_path,
moving_image=moving_image_path,
parameters=[parameter_file2],