def pipeline(self):
args = self.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_num)
from deepneuro.pipelines.Segment_GBM.predict import predict_GBM
predict_GBM(args.output_folder,
args.T2,
args.T1,
args.T1POST,
args.FLAIR,
None,
args.input_directory,
bias_corrected=args.debiased,
resampled=args.resampled,
registered=args.registered,
skullstripped=args.skullstripped,
normalized=args.normalized,
save_preprocess=args.save_preprocess,
save_all_steps=args.save_all_steps,
output_wholetumor_filename=args.wholetumor_output,
output_enhancing_filename=args.enhancing_output)
def test_glioblastoma_module(testing_directory="/home/DeepNeuro/tmp", gpu_num='0'):
import numpy as np
import os
from shutil import rmtree
FLAIR, T1POST, T1PRE = np.random.normal(loc=1000, scale=200, size=(240, 240, 40)), \
np.random.normal(loc=1500, scale=200, size=(240, 240, 180)), \
np.random.normal(loc=1300, scale=200, size=(120, 120, 60))
from deepneuro.utilities.conversion import save_data
try:
os.mkdir(testing_directory)
FLAIR_file = save_data(FLAIR, os.path.join(testing_directory, 'FLAIR.nii.gz'))
T1PRE_file = save_data(T1PRE, os.path.join(testing_directory, 'T1PRE.nii.gz'))
T1POST_file = save_data(T1POST, os.path.join(testing_directory, 'T1POST.nii.gz'))
from deepneuro.pipelines.Segment_GBM.predict import predict_GBM
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_num)
predict_GBM(testing_directory,
T1POST=T1POST_file,
FLAIR=FLAIR_file,
T1PRE=T1PRE_file)
rmtree(testing_directory)
except:
rmtree(testing_directory)
raise
return
def pipeline(self):
args = self.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_num)
from deepneuro.pipelines.Segment_GBM.predict import predict_GBM
predict_GBM(args.output_folder,
FLAIR=args.FLAIR,
T1POST=args.T1POST,
T1PRE=args.T1,
ground_truth=None,
bias_corrected=args.debiased,
registered=args.registered,
skullstripped=args.skullstripped,
preprocessed=args.preprocessed,
save_only_segmentations=args.save_only_segmentations,
output_probabilities=args.output_probabilities,
save_all_steps=args.save_all_steps,
output_wholetumor_filename=args.wholetumor_output,
output_enhancing_filename=args.enhancing_output,
quiet=args.quiet)
-registered: If flagged, data is assumed to already have been registered into the same space, and skips that preprocessing step.
-save_all_steps: If flagged, intermediate volumes in between preprocessing steps will be saved in output_folder.
-save_preprocessed: If flagged, the final volume after all preprocessing steps will be saved in output_folder
''')
parser.add_argument('--output_folder', type=str)
parser.add_argument('--T2', type=str)
parser.add_argument('--T1', type=str)
parser.add_argument('--T1POST', type=str)
parser.add_argument('--FLAIR', type=str)
parser.add_argument('--gpu_num', nargs='?', const='0', type=str)
parser.add_argument('--debiased', action='store_true')
parser.add_argument('--resampled', action='store_true')
parser.add_argument('--registered', action='store_true')
parser.add_argument('--skullstripped', action='store_true') # Currently non-functional
parser.add_argument('--normalized', action='store_true')
parser.add_argument('--save_preprocess', action='store_true')
parser.add_argument('--save_all_steps', action='store_true')
parser.add_argument('--ModelName', type=str)
parser.add_argument('--Output_WholeTumor', type=str)
parser.add_argument('--Output_EnhancingTumor', type=str)
args = parser.parse_args(sys.argv[2:])
#
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_num)
from deepneuro.pipelines.Segment_GBM.predict import predict_GBM
predict_GBM(args.output_folder, T2=args.T2, T1=args.T1, T1POST=args.T1POST, FLAIR=args.FLAIR, ground_truth=None, input_directory='/INPUT_DATA', bias_corrected=args.debiased, resampled=args.resampled, registered=args.registered, skullstripped=args.skullstripped, normalized=False, save_preprocess=False, save_all_steps=False, output_wholetumor_filename=args.Output_WholeTumor, output_enhancing_filename=args.Output_EnhancingTumor)
