def prepare_feature_for_regression(setting,
exp_dict=None,
feature_set=None,
cn_list=None):
feature_array = None
tre_array = None
if exp_dict['data'] == 'DIR-Lab_COPD':
setting_exp = su.initialize_setting(exp_dict['experiment'],
data=exp_dict['data'])
if cn_list is None:
cn_list = setting_exp['cn_range']
setting_exp['neighborhood_radius'] = int(
exp_dict['neighborhood_radius'])
landmarks, landmarks_merged, _ = read_landmarks_pkl(
setting_exp, cn_list=cn_list, exp_list=[exp_dict['experiment']])
feature_list = su.get_feature_set(feature_set)
for i_feature, feature in enumerate(feature_list):
if i_feature == 0:
feature_array = landmarks_merged[
exp_dict['experiment']][feature]
tre_array = landmarks_merged[
exp_dict['experiment']]['TRE_nonrigid']
else:
feature_array = np.concatenate(
(feature_array,
landmarks_merged[exp_dict['experiment']][feature]),
axis=1)
else:
raise ValueError('data: ' + exp_dict['data'] + ' is not valid')
exp_dict_name = exp_dict['data'] + '_' + exp_dict[
'experiment'] + '_' + exp_dict['neighborhood_radius']
if exp_dict['iteration'] is not None:
exp_dict_name = exp_dict_name + '_itr'
for itr in exp_dict['iteration']:
exp_dict_name = exp_dict_name + '_' + str(itr)
exp_dict_name = exp_dict_name + '_' + feature_set
return feature_array, tre_array, exp_dict_name
def do_elastix_registration():
cn_range = np.arange(1, 11)
where_to_run = 'sharkCluster' # 'local' , 'sharkCluster' , 'shark'
database = 'DIR-Lab_COPD'
current_experiment = 'elastix1'
if not su.load_setting(current_experiment, data=database, where_to_run=where_to_run):
registration_method = 'elastix'
setting = su.initialize_setting(current_experiment, data=database, where_to_run=where_to_run, registration_method=registration_method)
setting['affine_experiment'] = 'elastix1' # you can choose to use the affine result of the current experiment or from another experiment.
# This is useful when you want to tune the nonrigid registration. if it is None, then the affine folder in the current experiment is used
setting['affine_experiment_step'] = 0
setting['BSplineGridExperiment'] = 'elastix1'
setting['AffineParameter'] = ['par0049.affine.txt']
setting['MaskName_Affine'] = ['Torso']
setting['MaskName_BSpline'] = 'Lung_Atlas'
setting['MaskName_BSpline_Final'] = 'Lung_Atlas'
setting['BSplineGridParameter'] = 'par0049.bspline.grid.txt'
setting['BSplineParameter'] = 'par0049_stdT-advanced.txt'
setting['BSplineParameter_final'] = 'par0049_stdTL-advanced.txt'
setting['numberOfThreads'] = 7
su.write_setting(setting)
else:
setting = su.load_setting(current_experiment, data=database, where_to_run=where_to_run)
setting['cluster_phase'] = 1 # 0: affine, 1: initial perturb + BSpline, 2:final perturb + BSpline
setting = check_input_arguments(setting) # if script have some arguments, it goes to 'sharkCluster' mode. Now you can modify the code after submitting the jobs.
if setting['whereToRun'] == 'local' or setting['whereToRun'] == 'shark':
backup_script_address = backup_script(setting, os.path.realpath(__file__))
for cn in range(1, 11):
# reg_elx.affine(setting, cn=cn)
# reg_elx.affine_transform(setting, cn=cn)
hi = 1
for out in range(0, 1):
# reg_elx.perturbation(setting, cn=cn, out=out)
# reg_elx.bspline(setting, cn=cn, out=out)
# reg_elx.correct_initial_transform(setting, cn=cn, out=out)
# reg_elx.bspline_transform(setting, cn=cn, out=out, dvf=True)
hi = 1
for outfinal in range(1, 21):
# reg_elx.perturbation(setting, cn=cn, outfinal=outfinal)
# reg_elx.bspline_final(setting, cn=cn, outfinal=outfinal)
# reg_elx.correct_initial_transform(setting, cn=cn, outfinal=outfinal)
# reg_elx.bspline_final_transform(setting, cn=cn, outfinal=outfinal)
hi = 1
elif setting['whereToRun'] == 'sharkCluster':
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--cn', metavar='N', type=int, nargs='+',
help='an integer for the accumulator')
parser.add_argument('--out', metavar='N', type=int, nargs='+',
help='an integer for the accumulator')
parser.add_argument('--outfinal', metavar='N', type=int, nargs='+',
help='an integer for the accumulator')
parser.add_argument('--phase', metavar='N', type=int, nargs='+',
help='an integer for the accumulator')
args = parser.parse_args()
if args.phase is not None:
# clusterMode is in the run mode
phase = args.phase[0]
if args.cn is not None:
cn = args.cn[0]
if args.out is not None:
out = args.out[0]
if args.outfinal is not None:
outfinal = args.outfinal[0]
if phase == 0:
logging.debug('phase={}, cn={} '.format(phase, cn))
reg_elx.affine(setting, cn=cn)
if phase == 1:
logging.debug('phase={}, cn={}, out={} '.format(phase, cn, out))
reg_elx.perturbation(setting, cn=cn, out=out)
reg_elx.bspline(setting, cn=cn, out=out)
reg_elx.bspline_transform(setting, cn=cn, out=out, dvf=True)
if phase == 2:
logging.debug('phase={}, cn={}, outfinal={} '.format(phase, cn, outfinal))
reg_elx.perturbation(setting, cn=cn, outfinal=outfinal)
reg_elx.bspline_final(setting, cn=cn, outfinal=outfinal)
else:
# clusterMode is in the preparing_jobs mode
backup_script_address = backup_script(setting, os.path.realpath(__file__))
phase = setting['cluster_phase']
if phase == 0:
for cn in cn_range:
if not os.path.isfile(su.address_generator(setting, 'affineTransformParameter', cn=cn)):
job_name = setting['current_experiment'] + '_' + 'affine_cn_' + str(cn)
reg_elx.write_and_submit_job(setting, job_name=job_name, phase=phase, cn=cn, script_address=backup_script_address)
if phase == 1:
for cn in cn_range:
for out in range(0, 1):
job_name = setting['current_experiment'] + '_' + 'nonRigid_cn_' + str(cn) + '_out_' + str(out)
reg_elx.write_and_submit_job(setting, job_name=job_name, phase=phase, cn=cn, out=out, script_address=backup_script_address)
if phase == 2:
for cn in cn_range:
for outfinal in range(1, 21):
# if not os.path.isfile(su.address_generator(setting, 'DVF_nonRigid_composed_final', IN=cn, outfinal=outfinal)):
job_name = setting['current_experiment'] + '_' + 'nonRigid_cn_' + str(cn) + '_outfinal_' + str(outfinal)
reg_elx.write_and_submit_job(setting, job_name=job_name, phase=phase, cn=cn, outfinal=outfinal, script_address=backup_script_address)
def do_ANTs_registration():
cn_range = np.arange(1, 11)
out_range = np.arange(0, 21)
outfinal_range = np.arange(1, 21)
where_to_run = 'sharkCluster' # 'local' , 'sharkCluster' , 'shark'
database = 'DIR-Lab_COPD'
current_experiment = 'ANTs1'
if not su.load_setting(
current_experiment, data=database, where_to_run=where_to_run):
registration_method = 'ANTs'
setting = su.initialize_setting(
current_experiment,
data=database,
where_to_run=where_to_run,
registration_method=registration_method)
# setting['initial_nonrigid_iterations'] = [150, 120, 100, 30]
setting['NonRigidParameter'] = 'nonrigid.txt'
setting['NonRigidParameter_final'] = 'nonrigid_final.txt'
setting['ImageType_Registration'] = 'Im_Masked_Normalized'
setting['MaskName_Affine'] = ['Torso']
setting['useMask'] = False
setting['MaskName_BSpline'] = 'Lung_Filled' # Lung_Filled
setting['MaskName_BSpline_Final'] = 'Lung_Filled'
setting[
'affine_experiment'] = 'elastix1' # you can choose to use the affine result of the current experiment or from another experiment.
# This is useful when you want to tune the nonrigid registration. if it is None, then the affine folder in the current experiment is used
setting['affine_experiment_step'] = 0
su.write_setting(setting)
else:
setting = su.load_setting(current_experiment,
data=database,
where_to_run=where_to_run)
setting[
'cluster_phase'] = 1 # 0: affine, 1: initial perturb + BSpline_SyN, 2:final perturb + BSpline_Syn
setting['cluster_task_despendency'] = True
setting = check_input_arguments(
setting
) # if script have some arguments, it goes to 'sharkCluster' mode. Now you can modify the code after submitting the jobs.
if setting['whereToRun'] == 'local' or setting['whereToRun'] == 'shark':
backup_script_address = backup_script(setting,
os.path.realpath(__file__))
for cn in [2]:
# regANTs.affine_ANTs(setting, cn=cn)
# regANTs.affine_ANTs_transform(setting, cn=cn)
# regANTs.affine_ANTs_transform_image(setting, cn=cn)
hi = 1
for out in range(0, 1):
# regANTs.perturbation(setting, cn=cn, out=out)
# regANTs.BSpline_SyN_ANTs(setting, cn=cn, out=out)
# regANTs.BSpline_SyN_ANTs_transform(setting, cn=cn, out=out)
# regANTs.BSpline_SyN_ANTs_cleanup(setting, IN=cn, out=out)
# regANTs.BSpline_SyN_ANTs_transform_image(setting, IN=cn, out=out)
# regANTs.convert_nii2mha(setting, cn=cn, out=out)
hi = 1
for outfinal in range(1, 21):
# regANTs.perturbation(setting, cn=cn, outfinal=outfinal)
# regANTs.bspline_syn_ants_final(setting, cn=cn, outfinal=outfinal)
# regANTs.BSpline_SyN_ANTs_final_transform(setting, cn=cn, outfinal=outfinal)
# regANTs.BSpline_SyN_ANTs_cleanup_final(setting, cn=cn, outfinal=outfinal)
hi = 1
elif setting['whereToRun'] == 'sharkCluster':
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--cn',
metavar='N',
type=int,
nargs='+',
help='an integer for the accumulator')
parser.add_argument('--out',
metavar='N',
type=int,
nargs='+',
help='an integer for the accumulator')
parser.add_argument('--outfinal',
metavar='N',
type=int,
nargs='+',
help='an integer for the accumulator')
parser.add_argument('--phase',
metavar='N',
type=int,
nargs='+',
help='an integer for the accumulator')
args = parser.parse_args()
if args.phase is not None:
# clusterMode is in the run mode
phase = args.phase[0]
if args.cn is not None:
cn = args.cn[0]
if args.out is not None:
out = args.out[0]
if args.outfinal is not None:
outfinal = args.outfinal[0]
if phase == 0:
logging.debug('phase={}, cn={} '.format(phase, cn))
regANTs.affine_ANTs(setting, cn=cn)
regANTs.affine_ANTs_transform(setting, cn=cn)
regANTs.affine_ANTs_transform_image(setting, cn=cn)
if phase == 1:
logging.debug('phase={}, cn={}, out={} '.format(
phase, cn, out))
regANTs.perturbation(setting, cn=cn, out=out)
regANTs.BSpline_SyN_ANTs(setting, cn=cn, out=out)
regANTs.BSpline_SyN_ANTs_transform(setting, cn=cn, out=out)
regANTs.BSpline_SyN_ANTs_cleanup(setting, IN=cn, out=out)
regANTs.BSpline_SyN_ANTs_transform_image(setting,
IN=cn,
out=out)
if phase == 2:
logging.debug('phase={}, cn={}, outfinal={} '.format(
phase, cn, outfinal))
regANTs.perturbation(setting, cn=cn, outfinal=outfinal)
regANTs.bspline_syn_ants_final(setting,
cn=cn,
outfinal=outfinal)
regANTs.BSpline_SyN_ANTs_final_transform(setting,
cn=cn,
outfinal=outfinal)
regANTs.BSpline_SyN_ANTs_cleanup_final(setting,
cn=cn,
outfinal=outfinal)
else:
# clusterMode is in the preparing_jobs mode
backup_script_address = backup_script(setting,
os.path.realpath(__file__))
phase = setting['cluster_phase']
if phase == 0:
for cn in cn_range:
if not os.path.isfile(
su.address_generator(setting, 'affineDVF', cn=cn)):
job_name = setting[
'current_experiment'] + '_' + 'affine_cn_' + str(
cn)
regANTs.write_and_submit_job(
setting,
job_name=job_name,
phase=phase,
cn=cn,
script_address=backup_script_address)
if phase == 1:
for cn in range(9, 11):
for out in range(1, 21):
job_name = setting[
'current_experiment'] + '_' + 'nonRigid_cn_' + str(
cn) + '_out_' + str(out)
regANTs.write_and_submit_job(
setting,
job_name=job_name,
phase=phase,
cn=cn,
out=out,
script_address=backup_script_address)
if phase == 2:
for cn in range(9, 11):
for outfinal in range(1, 21):
if not os.path.isfile(
su.address_generator(
setting,
'DVF_nonRigid_composed_final',
cn=cn,
outfinal=outfinal)):
job_name = setting[
'current_experiment'] + '_' + 'nonRigid_cn_' + str(
cn) + '_outfinal_' + str(outfinal)
regANTs.write_and_submit_job(
setting,
job_name=job_name,
phase=phase,
cn=cn,
outfinal=outfinal,
script_address=backup_script_address)
def main(current_experiment=None):
where_to_run = 'local' # 'local' , 'sharkCluster' , 'shark'
database = 'DIR-Lab_COPD'
current_experiment = 'elastix1' # 'elastix1', 'ANTs1'
if not su.load_setting(
current_experiment, data=database, where_to_run=where_to_run):
registration_method = 'ANTs' # elastix , ANTs
setting = su.initialize_setting(
current_experiment,
data=database,
where_to_run=where_to_run,
registration_method=registration_method)
else:
setting = su.load_setting(current_experiment,
data=database,
where_to_run=where_to_run)
cn_list = setting['cn_range']
feature_list = [
'stdT', 'E_T', 'stdT_final', 'E_T_final', 'Jac', 'MIND', 'CV', 'NC',
'MI'
]
feature_pool_list = [
'stdT', 'E_T', 'stdT_final', 'E_T_final', 'Jac', 'MIND', 'CV'
]
cn_remove = []
for cn in cn_list:
# ------------ pre-processing -------------
pre.dirlab.dir_lab_copd(cn)
pre.cylinder_mask(setting, cn=cn)
pre.chest_segmentation.ptpulmo_segmentation_dirlab(
data='DIR-Lab_COPD', cn=cn, segment_organ='Torso')
pre.chest_segmentation.ptpulmo_segmentation_dirlab(
data='DIR-Lab_COPD', cn=cn, segment_organ='Lung')
pre.chest_segmentation.lung_fill_hole_dirlab(data='DIR-Lab_COPD',
cn=cn)
# pre.chest_segmentation.lung_fill_hole_erode(setting, cn=cn)
# pre.cylinder_mask(setting, cn=cn, overwrite=False)
pre.ants_preprocessing.image_normalization(setting, cn=cn)
# ------------ registration --------------
# preferably do the registration by running either do_elastix_registration or do_ANTs_registration
# do_elastix_registration
# ------------ post-processing -------------
reg_ants.convert_nii2mha(setting, cn=cn)
# ---------- feature extraction -----------
ut.std_t(setting, cn=cn, mode='initial')
ut.std_t(setting, cn=cn, mode='final')
ut.jac(setting, cn=cn)
output_error = ut.compute_error(setting, cn=cn, write_error_image=True)
if output_error == 2:
cn_remove.append(cn)
ut.ncmi(setting, cn=cn)
ut.feature_pooling(setting, cn=cn, feature_list=feature_pool_list)
cn_list = [cn for cn in cn_list if cn not in cn_remove]
landmarks = lu.load_features(setting,
feature_list=feature_list,
cn_list=cn_list)
exp_tre_list = ['elastix1-TRE_nonrigid', 'ANTs1-TRE_nonrigid']
exp_loss = ['ANTs1']
cn_list = setting['cn_range']
lu.plot.boxplot_tre(setting, cn_list=cn_list, exp_tre_list=exp_tre_list)
lu.plot.table_tre(setting, cn_list=cn_list, exp_tre_list=exp_tre_list)
lu.plot.table_tre_cn(setting, cn_list=cn_list, exp='ANTs1-TRE_nonrigid')
ants_loss.plot_loss(setting,
cn_list=np.arange(1, 11),
exp_list=exp_loss,
out=0)