def find_normalization_params(pat_dce, pat_gt, label, pat_model):
# Create the normalization object and load the model
dce_norm = StandardTimeNormalization(DCEModality())
dce_norm.load_model(pat_model)
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label, pat_gt)
# Find the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod, cat=label[0])
return dce_norm
from protoclass.data_management import DCEModality
from protoclass.data_management import GTModality
from protoclass.preprocessing import StandardTimeNormalization
# Define the path for the DCE
path_dce = '/data/prostate/experiments/Patient 383/DCE'
# Define the list of path for the GT
path_gt = ['/data/prostate/experiments/Patient 383/GT_inv/prostate']
# Define the associated list of label for the GT
label_gt = ['prostate']
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(path_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Create the object to normalize the DCE data
dce_norm = StandardTimeNormalization(dce_mod)
# Fit the data to get the normalization parameters
dce_norm.partial_fit_model(dce_mod, ground_truth=gt_mod,
cat='prostate')
print dce_norm.model_
# Define the path for the DCE
if os.path.isdir(os.path.join(path_patients, name))]
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(os.path.join(path_patients, id_patient,
path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append([os.path.join(path_patients, id_patient,
path_gt)])
for p_dce, p_gt, pat in zip(path_patients_list_dce, path_patients_list_gt,
id_patient_list):
print 'Processing #{}'.format(pat)
# Create the Tofts Extractor
tofts_ext = ToftsQuantificationExtraction(DCEModality(), T10, CA)
# Read the DCE
print 'Read DCE images'
dce_mod = DCEModality()
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Fit the parameters for Tofts
print 'Extract Tofts parameters'
tofts_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0], fit_aif=True)
]
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(
os.path.join(path_patients, id_patient, path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append(
[os.path.join(path_patients, id_patient, path_gt)])
for p_dce, p_gt, pat in zip(path_patients_list_dce, path_patients_list_gt,
id_patient_list):
print 'Processing #{}'.format(pat)
# Create the Tofts Extractor
pun_ext = PUNQuantificationExtraction(DCEModality())
# Read the DCE
print 'Read DCE images'
dce_mod = DCEModality()
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Fit the parameters for Brix
print 'Extract Weibull'
pun_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(os.path.join(path_patients, id_patient,
path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append([os.path.join(path_patients, id_patient, gt)
for gt in path_gt])
# Load all the data once. Splitting into training and testing will be done at
# the cross-validation time
for idx_pat in range(len(id_patient_list)):
print 'Read patient {}'.format(id_patient_list[idx_pat])
# Load the testing data that correspond to the index of the LOPO
# Create the object for the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(path_patients_list_dce[idx_pat])
print 'Read the DCE data for the current patient ...'
# Create the corresponding ground-truth
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt,
path_patients_list_gt[idx_pat])
print 'Read the GT data for the current patient ...'
# Load the approproate normalization object
filename_norm = (id_patient_list[idx_pat].lower().replace(' ', '_') +
'_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
if os.path.isdir(os.path.join(path_patients, name))]
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(os.path.join(path_patients, id_patient,
path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append([os.path.join(path_patients, id_patient,
path_gt)])
for p_dce, p_gt, pat in zip(path_patients_list_dce, path_patients_list_gt,
id_patient_list):
print 'Processing #{}'.format(pat)
# Create the Tofts Extractor
brix_ext = BrixQuantificationExtraction(DCEModality())
# Read the DCE
print 'Read DCE images'
dce_mod = DCEModality()
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Fit the parameters for Brix
print 'Extract Brix'
brix_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
# Generate the different path to be later treated
path_patients_list_dce = []
path_patients_list_gt = []
# Create the generator
id_patient_list = (name for name in os.listdir(path_patients)
if os.path.isdir(os.path.join(path_patients, name)))
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(os.path.join(path_patients, id_patient,
path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append([os.path.join(path_patients, id_patient,
path_gt)])
# Create the model iteratively
dce_norm = StandardTimeNormalization(DCEModality())
for pat_dce, pat_gt in zip(path_patients_list_dce, path_patients_list_gt):
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, pat_gt)
# Fit the model
dce_norm.partial_fit_model(dce_mod, ground_truth=gt_mod,
cat=label_gt[0])
# Define the path where to store the model
path_store_model = '/data/prostate/pre-processing/lemaitre-2016-nov/model'
]
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(
os.path.join(path_patients, id_patient, path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append(
[os.path.join(path_patients, id_patient, path_gt)])
for p_dce, p_gt, pat in zip(path_patients_list_dce, path_patients_list_gt,
id_patient_list):
print 'Processing #{}'.format(pat)
# Create the Tofts Extractor
semi_ext = SemiQuantificationExtraction(DCEModality())
# Read the DCE
print 'Read DCE images'
dce_mod = DCEModality()
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Load the approproate normalization object
filename_norm = (pat.lower().replace(' ', '_') + '_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
