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import os
import os.path
from os import environ
import sys
import json
import subprocess
import time
import nibabel as nib
# +++++++++++++ Conversion imports +++++++++++++++++++++++++
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.abspath(".."))
# +++++++++++++ Conversion imports +++++++++++++++++++++++++
from utils import *
from dicom_to_nii import convert_ct_dicom_to_nii, convert_transform_mr_to_nii, PatientList, save_images
from nii_to_dicom import convert_nii_to_dicom, integer_to_onehot
from predict_nnunet import predictNNUNet
def predict(tempPath, patient_id, regSeriesInstanceUID, runInterpreter):
# Important: Check the input parameters #################
if not patient_id or patient_id == "":
sys.exit("No Patient dataset loaded: Load the patient dataset in Study Management.")
if not regSeriesInstanceUID or regSeriesInstanceUID == "":
sys.exit("No series instance UID for Modality 'REG' file. Check for REG file in your study")
dir_base = os.path.join(tempPath, patient_id)
createdir(dir_base)
dir_ct_dicom = os.path.join(dir_base, 'ct_dicom')
createdir(dir_ct_dicom)
dir_mr_dicom = os.path.join(dir_base, 'mr_dicom')
createdir(dir_mr_dicom)
dir_reg_dicom = os.path.join(dir_base, 'reg_dicom')
createdir(dir_reg_dicom)
nnUNet_raw = os.path.join(os.getcwd(), 'nnUNet_raw')
nnUNet_preprocessed = os.path.join(os.getcwd(), 'nnUNet_preprocessed')
RESULTS_FOLDER = os.path.join(os.getcwd(), 'nnUNet_trained_models')
dataset = "Dataset103_EPTN_T1_CT_all_structures"
# IMPORTANT: data set modality: MR or CT ######################
predictType='MR'
# IMPORTANT DOT Remove ########################################
os.environ['nnUNet_raw'] = nnUNet_raw
os.environ['nnUNet_preprocessed'] = nnUNet_preprocessed
os.environ['nnUNet_results'] = RESULTS_FOLDER
# Important ++++++++++++++++++++++++++++++++++++++++++++++++
# Import the lib after setting environ parameters
# import nnunet.inference.predict_simple as nnunetpredict
print('** The python enviornment path: ', os.environ["PATH"])
# For nnunet version 2
import nnunetv2.inference.predict_from_raw_data as nnunetpredict
# ###########################################################
# predicted files
predictedNiiFile = os.path.join(tempPath, patient_id, 'predict_nii')
createdir(predictedNiiFile)
predictedDicom = os.path.join(tempPath, patient_id, 'predicted_dicom')
createdir(predictedDicom)
predictedDicomFile = os.path.join(predictedDicom, 'predicted_rtstruct.dcm')
print('** Use python interpreter: ', runInterpreter)
print('** Patient name: ', patient_id)
print('** REG series instance UID: ', regSeriesInstanceUID)
# Convert CT image to NII #############
startTime = time.time()
if predictType == 'CT':
dir_dicom_to_nii = os.path.join(nnUNet_raw, 'nnUNet_raw_data', 'Dataset098_HAN_nodes')
createdir(dir_dicom_to_nii)
downloadSeriesInstanceByModality(instanceID, dir_ct_dicom, "CT")
print("Loading CT from Orthanc done: ", time.time()-startTime)
# Convert CT image to NII #############
refCT= convert_ct_dicom_to_nii(dir_dicom=dir_ct_dicom, dir_nii=dir_dicom_to_nii, outputname='1a_001_0000.nii.gz', newvoxelsize = None)
print("Convert CT image to NII Done: ", time.time()-startTime)
# new version 2:
cmd = [modelPath, '-i', dir_dicom_to_nii, '-o', predictedNiiFile, '-d', dataset, '-tr', 'nnUNetTrainer_650epochs', '-c', '3d_fullres', '-f', '0']
out = subprocess.check_output(cmd)
# Important ########################
sys.argv = cmd
# #### nnunet version 2 #############
nnunetpredict.predict_entry_point()
print("Prediction CT done", time.time()-startTime)
niiFile = os.path.join(predictedNiiFile, '1a_001.nii.gz')
# POSTPROCESSING TO CONVERT FROM INTEGERS TO 2**i, ADD CONTOURS EXISTS, AND SMOOTH
integer_to_onehot(niiFile)
print("POST processing convert from integers done: ", time.time()-startTime)
startTime = time.time()
convert_nii_to_dicom(dicomctdir=dir_ct_dicom, predictedNiiFile=niiFile, predictedDicomFile=predictedDicomFile,
predicted_structures=predicted_structures, rtstruct_colors=rtstruct_colors, refCT=refCT)
print("Convert CT predicted NII to DICOM done: ", time.time()-startTime)
elif predictType == 'MR':
dir_dicom_to_nii = os.path.join(nnUNet_raw, 'nnUNet_raw_data',dataset)
createdir(dir_dicom_to_nii)
# Download the REG dicom ##############
downloadSeriesInstanceByModality(regSeriesInstanceUID, dir_reg_dicom, "REG")
print("Loading REG from Orthanc done: ", time.time()-startTime)
# Download the MR dicom ###############
# Read the mr study instance UID from the download REG dicom
mrSeriesInstanceUID = getSeriesInstanceUIDFromRegDicom(dir_reg_dicom, regSeriesInstanceUID)
downloadSeriesInstanceByModality(mrSeriesInstanceUID, dir_mr_dicom, "MR")
print("Loading MR from Orthanc done: ", time.time()-startTime)
# Execute REG tranformation ###########
ctSeriesInstanceUIDFromRegDicom = getCTSeriesInstanceUIDFromRegDicom(dir_reg_dicom, regSeriesInstanceUID)
print("CT Series Instance UID referenced by Reg dicom: ", ctSeriesInstanceUIDFromRegDicom)
downloadSeriesInstanceByModality(ctSeriesInstanceUIDFromRegDicom, dir_ct_dicom, "CT")
Patients = PatientList()
Patients.list_dicom_files(dir_ct_dicom, 1)
patient = Patients.list[0]
patient_name = patient.PatientInfo.PatientName
patient.import_patient_data(newvoxelsize=None)
CT = patient.CTimages[0]
startTime = time.time()
mr_reg = regMatrixTransformation(dir_mr_dicom, reg_file_path=dir_reg_dicom, regSeriesInstanceUID=regSeriesInstanceUID, CT=CT)
print("Transforming MR data done (OpenTPS.Core)")
# Convert transform MR image to NII ##################
refMR = convert_transform_mr_to_nii(dir_mr_dicom=dir_mr_dicom, tranform_mr = mr_reg, dir_nii=dir_dicom_to_nii, outputname='1a_001_0000.nii.gz', CT=CT)
refCT= convert_ct_dicom_to_nii(dir_dicom=dir_ct_dicom, dir_nii=dir_dicom_to_nii, outputname='1a_001_0001.nii.gz', newvoxelsize = None)
print("Convert CT image to NII Done: ", time.time()-startTime)
print("Convert transform MR image to NII Done: ", time.time()-startTime)
print("## start MR running prediction ###############")
startTime = time.time()
# modelPath = '..\\..\\python_environments\\prediction-3.10.9\\Scripts\\nnUNetv2_predict.exe'
# cmd = [modelPath, '-i', dir_dicom_to_nii, '-o', predictedNiiFile, '-d', '99', '-c', '3d_fullres' , '--disable_tta', '-tr', 'nnUNetTrainer_650epochs', '-f', '1, 4']
predictNNUNet(os.path.join(RESULTS_FOLDER,dataset, 'nnUNetTrainer_650epochs__nnUNetPlans__3d_fullres'),
dir_dicom_to_nii,
predictedNiiFile,
[1])
print("Prediction MR done", time.time()-startTime)
startTime = time.time()
predicted_structures = ["background", "BRAIN", "AMYGDALAE", "BRAINSTEM", "CAUDATENUCLEI", "CEREBELLUM", "CHIASM", "COCHLEAS", "CORNEAS", "CORPUSCALLOSUM", "FORNICES", "GLANDPINEAL", "HIPPOCAMPI", "HYPOTHALAMI", "LACRIMALGLANDS", "LENSES", "OPTICNERVES", "ORBITOFRONTALS", "PITUITARY", "RETINAS", "THALAMI", "VSCCs"]
rtstruct_colors = [[255,0,0]]*len(predicted_structures)
niiFile = os.path.join(predictedNiiFile, '1a_001.nii.gz')
# POSTPROCESSING TO CONVERT FROM INTEGERS TO 2**i, ADD CONTOURS EXISTS, AND SMOOTH
integer_to_onehot(niiFile)
print("POST processing convert from integers done: ", time.time()-startTime)
# Convert CT image to NII #############
convert_nii_to_dicom(dicomctdir=dir_ct_dicom, predictedNiiFile=niiFile, predictedDicomFile=predictedDicomFile,
predicted_structures=predicted_structures, rtstruct_colors=rtstruct_colors, refCT=refCT)
else:
print("Not supported yet")
startTime = time.time()
uploadDicomToOrthanc(predictedDicomFile)
print("Upload predicted result to Orthanc done: ", time.time()-startTime)
# tempPath = 'C:\Temp\parrot_prediction'
# regSeriesInstanceUID = '1.2.246.352.205.5029381855449574337.1508502639685232062'
# runInterpreter = 'py3109'
# patientName = 'P0461C0006I7638639'
'''
Prediction parameters provided by the server. Select the parameters to be used for prediction:
[1] tempPath: The path where the predict.py is stored,
[2] patientname: python version,
[3] ctSeriesInstanceUID: Series instance UID for data set with modality = CT. To predict 'MR' modality data, retrieve the CT UID by the code (see Precision Code)
[4] rtStructSeriesInstanceUID: Series instance UID for modality = RTSTURCT
[5] regSeriesInstanceUID: Series instance UID for modality = REG,
[6] runInterpreter: The python version for the python environment
[7] oarList: only for dose predciton. For contour predicion oarList = []
[8] tvList: only for dose prediction. For contour prediction tvList = []
'''
if __name__ == '__main__':
predict(tempPath=sys.argv[1], patient_id=sys.argv[2], regSeriesInstanceUID=sys.argv[5], runInterpreter=sys.argv[6])
# predict(tempPath=tempPath, patient_id=patientName, regSeriesInstanceUID=regSeriesInstanceUID, runInterpreter=runInterpreter) |