def register_tobase_get_closest( testing_ct_filename, base_case_ct_filenames,
base_case_label_filenames, test_case_transfo_dir, is_register):
"""
registers the test ct scan to each of the base_case_ct_filenames
and finds the closest cagse according to some similarity metric
"""
toolsPaths = ['CIP_PATH'];
path=dict()
for path_name in toolsPaths:
path[path_name]=os.environ.get(path_name,False)
if path[path_name] == False:
print path_name + " environment variable is not set"
exit()
print("\n\n\n In register_tobase_get_closest \n\n\n")
registerLabelMaps = os.path.join(path['CIP_PATH'],"RegisterLabelMaps2D")
base_to_testing_transfo_names = [""]*len(base_case_ct_filenames)
for ii in range(0, len(base_case_ct_filenames)) :
base_case_ct_filename = base_case_ct_filenames[ii]
base_to_testing_transfo_names[ii] = os.path.join(test_case_transfo_dir, \
base_case_ct_filenames[ii].split('/')[-1].split('.')[0]+"_to_"+testing_ct_filename.split('/')[-1].split('.')[0]+"_tfm_0GenericAffine.tfm")
if(is_register is True):
#register_2d_ct( base_case_ct_filename, testing_ct_filename, base_to_testing_transfo_names[ii])
temp1_for_registration = base_case_ct_filename.split('.')[0]+"_thresholded.nrrd"
temp2_for_registration = testing_ct_filename.split('.')[0]+"_thresholded.nrrd"
print(registerLabelMaps+" -m "+temp1_for_registration+" -f "+\
temp2_for_registration+ " --outputTransform "+base_to_testing_transfo_names[ii])
#os.system(registerLabelMaps+" -m "+base_case_ct_filename+" -f "+\
# testing_ct_filename+ " --outputTransform "+base_to_testing_transfo_names[ii])
else:
exit()
"""
find closest base case label filename and the index in the list we have
"""
training_similarity_files = compute_similarity_from_filenames(testing_ct_filename, \
base_case_ct_filenames, base_case_label_filenames, \
base_to_testing_transfo_names)
closest_case_ct = getClosestCases(base_case_ct_filenames, \
training_similarity_files, "ncc", 1, 1)
"""
Return the ct filename of the closest base case and the corresponding
closest transformation
"""
return closest_case_ct[0,0], base_to_testing_transfo_names[\
base_case_ct_filenames.index(closest_case_ct[0,0])]
def register_tobase_get_closest(testing_ct_filename, base_case_ct_filenames,
base_case_label_filenames,
test_case_transfo_dir, is_register):
"""
registers the test ct scan to each of the base_case_ct_filenames
and finds the closest cagse according to some similarity metric
"""
toolsPaths = ['CIP_PATH']
path = dict()
for path_name in toolsPaths:
path[path_name] = os.environ.get(path_name, False)
if path[path_name] == False:
print path_name + " environment variable is not set"
exit()
print("\n\n\n In register_tobase_get_closest \n\n\n")
registerLabelMaps = os.path.join(path['CIP_PATH'], "RegisterLabelMaps2D")
base_to_testing_transfo_names = [""] * len(base_case_ct_filenames)
for ii in range(0, len(base_case_ct_filenames)):
base_case_ct_filename = base_case_ct_filenames[ii]
base_to_testing_transfo_names[ii] = os.path.join(test_case_transfo_dir, \
base_case_ct_filenames[ii].split('/')[-1].split('.')[0]+"_to_"+testing_ct_filename.split('/')[-1].split('.')[0]+"_tfm_0GenericAffine.tfm")
if (is_register is True):
#register_2d_ct( base_case_ct_filename, testing_ct_filename, base_to_testing_transfo_names[ii])
temp1_for_registration = base_case_ct_filename.split(
'.')[0] + "_thresholded.nrrd"
temp2_for_registration = testing_ct_filename.split(
'.')[0] + "_thresholded.nrrd"
print(registerLabelMaps+" -m "+temp1_for_registration+" -f "+\
temp2_for_registration+ " --outputTransform "+base_to_testing_transfo_names[ii])
#os.system(registerLabelMaps+" -m "+base_case_ct_filename+" -f "+\
# testing_ct_filename+ " --outputTransform "+base_to_testing_transfo_names[ii])
else:
exit()
"""
find closest base case label filename and the index in the list we have
"""
training_similarity_files = compute_similarity_from_filenames(testing_ct_filename, \
base_case_ct_filenames, base_case_label_filenames, \
base_to_testing_transfo_names)
closest_case_ct = getClosestCases(base_case_ct_filenames, \
training_similarity_files, "ncc", 1, 1)
"""
Return the ct filename of the closest base case and the corresponding
closest transformation
"""
return closest_case_ct[0,0], base_to_testing_transfo_names[\
base_case_ct_filenames.index(closest_case_ct[0,0])]
def compute_atlas_from_labelfiles(
input_vol,
testing_ct_filename,
training_ct_filenames,
training_labelmap_filenames,
base_case_ct_filenames,
base_case_labelmap_filenames,
test_case_transfo_dir,
transformation_filenames,
num_closest_cases,
similarity,
threshold_value_for_similarity,
):
"""
Compute atlas from a list of label files
Inputs
------
testing_ct_filename : full path of the ct filename to be segmented
training_ct_filenames : full path of the ct filenames in the training data
training_labelmap_filenames : full path of the labelmaps in the training data
base_case_ct_filenames : filenames of the base case IDs
test_case_transfo_dir : directory where the base to test transfos
and the transformed labelmaps will be stored
transformation_filenames["case ids"] : 1 line per labelmap filename. dict()
transfo from base id to test case
num_closest_cases : number of cases to be selected for atlas generation
similarity : string. type of similarity used to find closest cases
threshold_value_for_similarity : value below which cases won't be chosen
assumptions about naming and directories :
transformations to be computed in 1 specific folder : test_case_transfo_dir
transfo filenames will be name1_to_name2_postfix.tfm
resampled labelmaps : test_case_transfo_dir
masks of training data : place in same directory as traning data
"""
# set environment variables and function calls ..
toolsPaths = ["TEEM_PATH", "ITKTOOLS_PATH", "CIP_PATH"]
path = dict()
for path_name in toolsPaths:
path[path_name] = os.environ.get(path_name, False)
if path[path_name] == False:
print path_name + " environment variable is not set"
sys.exit()
prior_atlas = dict()
pec_classes = ["leftmajor", "leftminor", "rightmajor", "rightminor"]
label_value = dict()
closest_label_maps = dict()
closest_similarity_values = [0.0] * num_closest_cases
label_value["leftmajor"] = 13592
label_value["leftminor"] = 13336
label_value["rightmajor"] = 13591
label_value["rightminor"] = 13335
label_value["nonpec"] = 0
label_value["subqleft"] = 17944
label_value["subqright"] = 17943
resamplecall = os.path.join(path["CIP_PATH"], "ResampleLabelMap2D")
"""
compute transformations to all base cases and find closest base case id
Files required : full path ct file names of all base cases
"""
closest_base_ct, close_base_tfm = register_tobase_get_closest(
testing_ct_filename, base_case_ct_filenames, base_case_labelmap_filenames, test_case_transfo_dir, True
)
print ("the closest base case is : " + closest_base_ct + " " + close_base_tfm)
base_case_key = closest_base_ct.split("/")[-1].split(".")[0].rstrip("\n")
list_transfos_given_base = [""] * np.shape(transformation_filenames[base_case_key])[0]
for i in range(0, np.shape(transformation_filenames[base_case_key])[0]):
list_transfos_given_base[i] = transformation_filenames[base_case_key][i]
for class_index in pec_classes:
print ("constucting atlas for " + class_index)
"""
For each class, find the most similar cases by computing similarity.
Files required : full path ct slices of all training data, full path masks of all training data (to be
generated here), transformations from base to all training data.
"""
training_similarity_files = compute_dice_similarity_from_filenames(
testing_ct_filename,
training_ct_filenames,
training_labelmap_filenames,
list_transfos_given_base,
close_base_tfm,
)
closest_cases = getClosestCases(
training_labelmap_filenames,
training_similarity_files,
"dice",
num_closest_cases,
threshold_value_for_similarity,
)
num_closest_cases_nonzero = np.shape(filter(None, closest_cases[0]))[0]
closest_cases_tfms = [""] * num_closest_cases_nonzero
for i in range(0, num_closest_cases_nonzero):
closest_cases_tfms[i] = list_transfos_given_base[training_labelmap_filenames.index(closest_cases[0, i])]
imageFull, imageInfo = nrrd.read(testing_ct_filename)
closest_label_maps[class_index] = [
np.zeros([np.shape(imageFull)[0], np.shape(imageFull)[1], np.shape(imageFull)[2]], dtype=float)
] * num_closest_cases_nonzero
"""
resample all closest cases
Files required : labelmaps of all training data
"""
for i in range(0, num_closest_cases_nonzero):
# we assume that this transformation exists
training_to_base_tfm = closest_cases_tfms[i]
base_to_testing = close_base_tfm
source_file = closest_cases[0, i]
template_file = testing_ct_filename
dest_file = os.path.join(
test_case_transfo_dir, source_file.split("/")[-1] + "_to_" + template_file.split("/")[-1] + ".nrrd"
)
"""
multiple transformations required when the training case is not the base case.
"""
if close_base_tfm != closest_cases_tfms[i]:
resamp_call = (
resamplecall
+ " -d "
+ template_file
+ " -r "
+ dest_file
+ " -t "
+ training_to_base_tfm
+ ","
+ base_to_testing
+ " -l "
+ source_file
)
else:
"""
1 transformation required
"""
resamp_call = (
resamplecall
+ " -d "
+ template_file
+ " -r "
+ dest_file
+ " -t "
+ close_base_tfm
+ " -l "
+ source_file
)
print (resamp_call)
subprocess.call(resamp_call, shell=True)
""" load the registered labelmap .
exract left and right major/minor labelmaps and binarise. """
imageFulldest, imageInfo = nrrd.read(dest_file)
closest_label_maps[class_index][i] = np.zeros(
[np.shape(imageFull)[0], np.shape(imageFull)[1], np.shape(imageFull)[2]], dtype=float
)
the_labels = imageFulldest != label_value[class_index]
closest_label_maps[class_index][i][the_labels] = 0.0
the_labels = imageFulldest == label_value[class_index]
closest_label_maps[class_index][i][the_labels] = 1.0
closest_similarity_values[i] = closest_cases[1, i]
"""
average
"""
# normalise weights so that they sum to 1
closest_similarity_values_float = np.array(map(float, closest_similarity_values)) / np.sum(
np.array(map(float, closest_similarity_values))
)
for class_index in pec_classes:
prior_atlas[class_index] = construct_probabilistic_atlas(
closest_label_maps[class_index], True, np.array(map(float, closest_similarity_values_float)), None
)
"""
return atlases
"""
# return prior_atlas["leftmajor"], prior_atlas["leftminor"], prior_atlas["rightmajor"],prior_atlas["rightminor"]
return prior_atlas
def compute_atlas_from_labelfiles(input_vol, testing_ct_filename, training_ct_filenames,\
training_labelmap_filenames, base_case_ct_filenames, base_case_labelmap_filenames,\
test_case_transfo_dir, transformation_filenames, num_closest_cases, \
similarity, threshold_value_for_similarity):
"""
Compute atlas from a list of label files
Inputs
------
testing_ct_filename : full path of the ct filename to be segmented
training_ct_filenames : full path of the ct filenames in the training data
training_labelmap_filenames : full path of the labelmaps in the training data
base_case_ct_filenames : filenames of the base case IDs
test_case_transfo_dir : directory where the base to test transfos
and the transformed labelmaps will be stored
transformation_filenames["case ids"] : 1 line per labelmap filename. dict()
transfo from base id to test case
num_closest_cases : number of cases to be selected for atlas generation
similarity : string. type of similarity used to find closest cases
threshold_value_for_similarity : value below which cases won't be chosen
assumptions about naming and directories :
transformations to be computed in 1 specific folder : test_case_transfo_dir
transfo filenames will be name1_to_name2_postfix.tfm
resampled labelmaps : test_case_transfo_dir
masks of training data : place in same directory as traning data
"""
# set environment variables and function calls ..
toolsPaths = ['TEEM_PATH', 'ITKTOOLS_PATH', 'CIP_PATH']
path = dict()
for path_name in toolsPaths:
path[path_name] = os.environ.get(path_name, False)
if path[path_name] == False:
print path_name + " environment variable is not set"
sys.exit()
prior_atlas = dict()
pec_classes = ["leftmajor", "leftminor", "rightmajor", "rightminor"]
label_value = dict()
closest_label_maps = dict()
closest_similarity_values = [0.0] * num_closest_cases
label_value["leftmajor"] = 13592
label_value["leftminor"] = 13336
label_value["rightmajor"] = 13591
label_value["rightminor"] = 13335
label_value["nonpec"] = 0
label_value["subqleft"] = 17944
label_value["subqright"] = 17943
resamplecall = os.path.join(path['CIP_PATH'], "ResampleLabelMap2D")
"""
compute transformations to all base cases and find closest base case id
Files required : full path ct file names of all base cases
"""
closest_base_ct, close_base_tfm = register_tobase_get_closest(\
testing_ct_filename, base_case_ct_filenames, base_case_labelmap_filenames, \
test_case_transfo_dir, True)
print("the closest base case is : " + closest_base_ct + " " +
close_base_tfm)
base_case_key = closest_base_ct.split("/")[-1].split(".")[0].rstrip("\n")
list_transfos_given_base = [""] * np.shape(
transformation_filenames[base_case_key])[0]
for i in range(0, np.shape(transformation_filenames[base_case_key])[0]):
list_transfos_given_base[i] = transformation_filenames[base_case_key][
i]
for class_index in pec_classes:
print("constucting atlas for " + class_index)
"""
For each class, find the most similar cases by computing similarity.
Files required : full path ct slices of all training data, full path masks of all training data (to be
generated here), transformations from base to all training data.
"""
training_similarity_files = compute_dice_similarity_from_filenames(testing_ct_filename, \
training_ct_filenames, training_labelmap_filenames, \
list_transfos_given_base, close_base_tfm)
closest_cases = getClosestCases(training_labelmap_filenames, \
training_similarity_files, "dice", num_closest_cases, threshold_value_for_similarity)
num_closest_cases_nonzero = np.shape(filter(None, closest_cases[0]))[0]
closest_cases_tfms = [""] * num_closest_cases_nonzero
for i in range(0, num_closest_cases_nonzero):
closest_cases_tfms[i] = list_transfos_given_base[\
training_labelmap_filenames.index(closest_cases[0,i])]
imageFull, imageInfo = nrrd.read(testing_ct_filename)
closest_label_maps[class_index] =[np.zeros([np.shape(imageFull)[0], \
np.shape(imageFull)[1], np.shape(imageFull)[2]], dtype=float)] \
*num_closest_cases_nonzero
"""
resample all closest cases
Files required : labelmaps of all training data
"""
for i in range(0, num_closest_cases_nonzero):
# we assume that this transformation exists
training_to_base_tfm = closest_cases_tfms[i]
base_to_testing = close_base_tfm
source_file = closest_cases[0, i]
template_file = testing_ct_filename
dest_file = os.path.join(test_case_transfo_dir, \
source_file.split("/")[-1]+"_to_"+template_file.split("/")[-1]+".nrrd")
"""
multiple transformations required when the training case is not the base case.
"""
if (close_base_tfm != closest_cases_tfms[i]):
resamp_call = resamplecall+" -d "+template_file+" -r "+ \
dest_file+" -t "+training_to_base_tfm+","+ \
base_to_testing+" -l "+source_file
else:
"""
1 transformation required
"""
resamp_call = resamplecall+" -d "+template_file+" -r "+ \
dest_file+" -t "+close_base_tfm +" -l "+source_file
print(resamp_call)
subprocess.call(resamp_call, shell=True)
""" load the registered labelmap .
exract left and right major/minor labelmaps and binarise. """
imageFulldest, imageInfo = nrrd.read(dest_file)
closest_label_maps[class_index][i] =np.zeros([np.shape(imageFull)[0], \
np.shape(imageFull)[1], np.shape(imageFull)[2]], dtype=float)
the_labels = imageFulldest != label_value[class_index]
closest_label_maps[class_index][i][the_labels] = 0.0
the_labels = imageFulldest == label_value[class_index]
closest_label_maps[class_index][i][the_labels] = 1.0
closest_similarity_values[i] = closest_cases[1, i]
"""
average
"""
#normalise weights so that they sum to 1
closest_similarity_values_float = np.array(map(float, \
closest_similarity_values))/np.sum(np.array(map(float, \
closest_similarity_values)))
for class_index in pec_classes:
prior_atlas[class_index] = construct_probabilistic_atlas(closest_label_maps[class_index], \
True, np.array(map(float, closest_similarity_values_float)), None)
"""
return atlases
"""
#return prior_atlas["leftmajor"], prior_atlas["leftminor"], prior_atlas["rightmajor"],prior_atlas["rightminor"]
return prior_atlas
