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 validationscores(validationset,finalmasks):
#This function performs step 9 of the plan.
scores = []
for i in range(len(validationset)):
reference = findfixedmask3d(validationset[i])
result = finalmasks[i]
if sum(sum(sum(x) for x in y) for y in finalmasks[i]) == 0:
scores.append([0,0,-100])
continue
DCscore = medpyDC(result,reference)
HDscore = medpyHD(result,reference)
RVDscore = medpyRVD(result,reference)
scores.append([DCscore,HDscore,RVDscore])
return scores
#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],
output_dir=output_dir)
listofDCs = []
listofHDs = []
fixed_mask_array = findfixedmask2d(fixednr, slicenr)
for x in listofvalues:
replace(parameter_file, keyword, x)
runnr = register2d(fixednr, movingnr, slicenr, parameter_file)
transform2d(movingnr, slicenr, runnr, transformmask=True)
transformed_mask_path = os.path.join(f"results{runnr}",
r"transformedmask\result.mhd")
transformed_mask = sitk.ReadImage(transformed_mask_path)
transformed_mask_array = sitk.GetArrayFromImage(transformed_mask)
DCscore = medpyDC(transformed_mask_array, fixed_mask_array)
HDscore = medpyHD(transformed_mask_array, fixed_mask_array)
listofDCs.append(DCscore)
listofHDs.append(HDscore)
print(keyword)
print(listofvalues)
print('Dice scores')
print(listofDCs)
print('Hausendorff distances')
print(listofHDs)
fig, ax = plt.subplots(1, 2)
ax[0].plot(listofvalues, listofDCs)
ax[0].set_title('Dice coefficient scores')