def process3(folder):
""" Post process data """
print(folder)
util.setup(folder)
params = ['Mobility', 'Selfcare', 'Activity', 'Pain', 'Anxiety', 'karnofsky', 'Index_value']
image_ids = find_images()
result = util.post_calculations(image_ids)
print(len(result['all']))
# util.avg_calculation(result['all'], 'all_N=112', None, True, folder, save_sum=True)
# util.avg_calculation(result['img'], 'img_N=112', None, True, folder)
for qol_param in params:
if qol_param == "Delta_qol2":
(image_ids_with_qol, qol) = util.get_qol(image_ids, "Delta_qol")
qol = [-1 if _temp <= -0.15 else 0 if _temp < 0.15 else 1 for _temp in qol]
else:
(image_ids_with_qol, qol) = util.get_qol(image_ids, qol_param)
if qol_param not in ["karnofsky", "Delta_kps"]:
qol = [_temp * 100 for _temp in qol]
default_value = -100
print(qol_param)
print(len(qol))
result = util.post_calculations(image_ids_with_qol)
for label in result:
if label == 'img':
continue
print(label)
util.avg_calculation(result[label], label + '_' + qol_param + '_N=112', qol, True, folder, default_value=default_value)
util.median_calculation(result[label], label + '_' + qol_param + '_N=112', qol, True, folder, default_value=default_value)
def process2(folder):
""" Post process data Delta"""
print(folder)
util.setup(folder)
params = ['Delta_qol', 'Delta_qol2', 'Delta_mobility', 'Delta_selfcare', 'Delta_activity', 'Delta_pain', 'Delta_anixety', 'Delta_kps']
image_ids = find_images()
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'], 'all_N=112', None, True, folder, save_sum=True)
util.avg_calculation(result['img'], 'img_N=112', None, True, folder)
print("\n\n\n\n\n")
for qol_param in params:
if qol_param == "Delta_qol2":
(image_ids_with_qol, qol) = util.get_qol(image_ids, "Delta_qol")
qol = [-1 if _temp <= -0.15 else 0 if _temp < 0.15 else 1 for _temp in qol]
else:
(image_ids_with_qol, qol) = util.get_qol(image_ids, qol_param)
qol = [_temp * 100 for _temp in qol]
default_value = -300
print(qol_param, len(qol))
result = util.post_calculations(image_ids_with_qol)
for label in result:
if label == 'img':
continue
print(label)
# util.avg_calculation(result[label], label + '_' + qol_param, qol, True, folder, default_value=default_value)
util.median_calculation(result[label], label + '_' + qol_param, qol, True, folder, default_value=default_value)
def process(folder, censor_date, survival_groups, pids_to_exclude=None):
""" Post process data """
print(folder)
util.setup(folder)
image_ids, survival_days = util.get_image_id_and_survival_days(study_id="GBM_survival_time", exclude_pid=pids_to_exclude, censor_date_str=censor_date)
result = util.post_calculations(image_ids)
print('Total: ' + str(len(result['all'])) + ' patients')
util.avg_calculation(result['all'], 'tumor', None, True, folder, save_sum=True)
util.mortality_rate_calculation(result['all'], '_all_year', survival_days, True, folder, default_value=-1, max_value=150, per_year=True)
util.avg_calculation(result['img'], 'volume', None, True, folder)
image_ids, survival_days = util.get_image_id_and_survival_days(study_id="GBM_survival_time", exclude_pid=pids_to_exclude, censor_date_str=censor_date,resection=True)
result = util.post_calculations(image_ids)
print('Resected: ' + str(len(result['all'])) + ' patients')
util.avg_calculation(result['all'], 'tumor_resected', None, True, folder, save_sum=True)
util.avg_calculation(result['img'], 'volume_resected', None, True, folder)
util.mortality_rate_calculation(result['all'], '_resected_year', survival_days, True, folder, default_value=-1, max_value=150, per_year=True)
for group in survival_groups:
image_ids, survival_days = util.get_image_id_and_survival_days(study_id="GBM_survival_time", exclude_pid=pids_to_exclude, censor_date_str=censor_date,survival_group=group)
result = util.post_calculations(image_ids)
print('Group ' + str(group) + ': ' + str(len(result['all'])) + ' patients')
label = 'tumor_' + str(group[0]) + '-' + str(group[1])
util.avg_calculation(result['all'], label, None, True, folder, save_sum=True)
def process(folder, censor_date):
""" Post process data """
print(folder)
util.setup(folder)
image_ids, survival_days = util.get_image_id_and_survival_days(
study_id="GBM_survival_time", censor_date_str=censor_date)
result = util.post_calculations(image_ids)
print('Total: ' + str(len(result['all'])) + ' patients')
util.avg_calculation(result['all'],
'tumor',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result['img'], 'volume', None, True, folder)
util.mortality_rate_calculation(result['all'],
'',
survival_days,
True,
folder,
default_value=-1)
image_ids, survival_days = util.get_image_id_and_survival_days(
study_id="GBM_survival_time",
censor_date_str=censor_date,
resection=True)
result = util.post_calculations(image_ids)
print('Resected: ' + str(len(result['all'])) + ' patients')
util.avg_calculation(result['all'],
'tumor_resected',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result['img'], 'volume_resected', None, True, folder)
util.mortality_rate_calculation(result['all'],
'_resected',
survival_days,
True,
folder,
default_value=-1)
survival_groups = [[0, 182], [183, 730], [731, float('Inf')]]
for group in survival_groups:
image_ids, survival_days = util.get_image_id_and_survival_days(
study_id="GBM_survival_time",
censor_date_str=censor_date,
survival_group=group)
result = util.post_calculations(image_ids)
print('Group ' + str(group) + ': ' + str(len(result['all'])) +
' patients')
label = 'tumor_' + str(group[0]) + '-' + str(group[1])
util.avg_calculation(result['all'],
label,
None,
True,
folder,
save_sum=True)
def process(folder):
""" Post process data distribution and baseline"""
print(folder)
util.setup(folder)
params = ['Mobility', 'Selfcare', 'Activity', 'Pain', 'Anxiety', 'karnofsky', 'Index_value']
image_ids = find_images_163()
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'], 'all_N=163', None, True, folder, save_sum=True)
util.avg_calculation(result['img'], 'img_N=163', None, True, folder)
image_ids = do_img_registration_GBM.find_images()
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'], 'all_N=170', None, True, folder, save_sum=True)
util.avg_calculation(result['img'], 'img_N=170', None, True, folder)
for qol_param in params:
(image_ids_with_qol, qol) = util.get_qol(image_ids, qol_param)
if qol_param not in ["karnofsky", "Delta_kps"]:
qol = [_temp * 100 for _temp in qol]
default_value = -100
print(qol_param)
print(len(qol))
result = util.post_calculations(image_ids_with_qol)
for label in result:
if label == 'img':
continue
print(label)
# util.avg_calculation(result[label], label + '_' + qol_param, qol, True, folder, default_value=default_value)
util.median_calculation(result[label], label + '_' + qol_param, qol, True, folder, default_value=default_value)
def process(folder, exclude):
""" Post process data """
print(folder)
util.setup(folder, "meningiomer")
image_ids = find_images(exclude=exclude)
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'],
'all',
None,
True,
folder,
save_sum=True,
save_pngs=True)
util.avg_calculation(result['img'], 'img', None, True, folder)
def process(folder):
""" Post process data """
print(folder)
util.setup(folder)
for grade in [2, 3, 4]:
image_ids, survival_days = util.get_image_id_and_survival_days(
glioma_grades=[grade])
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'],
'all_',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result['img'], 'img_', None, True, folder)
for label in result:
if label == 'img':
continue
util.avg_calculation(result[label],
'survival_time_grade_' + str(grade),
survival_days,
True,
folder,
default_value=-1)
def process(folder, glioma_grades):
""" Post process data """
print(folder)
util.setup(folder)
params = [
'Index_value', 'Global_index', 'Mobility', 'Selfcare', 'Activity',
'Pain', 'Anxiety', 'karnofsky'
]
(image_ids, qol) = util.get_image_id_and_qol(None,
exclude_pid,
glioma_grades=glioma_grades)
print(len(image_ids))
result = util.post_calculations(image_ids)
print(len(result['all']))
util.avg_calculation(result['all'],
'all',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result['img'], 'img', None, True, folder)
# (image_ids, qol) = util.get_image_id_and_qol('Index_value', exclude_pid, glioma_grades=glioma_grades)
# result = util.post_calculations(image_ids)
# util.calculate_t_test(result['all'], 1)
for qol_param in params:
(image_ids, qol) = util.get_image_id_and_qol(qol_param, exclude_pid)
if not qol_param == "karnofsky":
qol = [_temp * 100 for _temp in qol]
if not qol_param == "Index_value":
default_value = -100
else:
default_value = 0
print(image_ids)
result = util.post_calculations(image_ids)
for label in result:
print(label)
if label == 'img':
continue
util.avg_calculation(result[label],
label + '_' + qol_param,
qol,
True,
folder,
default_value=default_value)
util.std_calculation(result[label], label + '_' + qol_param, qol,
True, folder)
def process(folder, pids_to_exclude=()):
""" Post process data """
print(folder)
util.setup(folder, 'MolekylareMarkorer')
conn = sqlite3.connect(util.DB_PATH, timeout=120)
conn.text_factory = str
cursor = conn.execute(
'''SELECT pid from MolekylareMarkorer ORDER BY pid''')
image_ids = []
image_ids_1 = []
image_ids_2 = []
image_ids_3 = []
tag_data_1 = []
tag_data_2 = []
tag_data_3 = []
img = nib.load("/media/leb/data/Atlas/lobes_brain.nii")
lobes_brain = img.get_data()
label_defs = util.get_label_defs()
res_right_left_brain = {}
res_lobes_brain = {}
patients = '\nPID MM\n----------------\n'
for pid in cursor:
pid = pid[0]
if pid in pids_to_exclude:
continue
_id = conn.execute('''SELECT id from Images where pid = ?''',
(pid, )).fetchone()
if not _id:
print("---No data for ", pid)
continue
_id = _id[0]
_mm = conn.execute(
"SELECT Subgroup from MolekylareMarkorer where pid = ?",
(pid, )).fetchone()[0]
if _mm is None:
print("No mm data for ", pid)
patients += str(pid) + ': ?\n'
continue
_desc = conn.execute(
"SELECT comments from MolekylareMarkorer where pid = ?",
(pid, )).fetchone()[0]
if _desc is None:
_desc = ""
_filepath = conn.execute(
"SELECT filepath_reg from Labels where image_id = ?",
(_id, )).fetchone()[0]
if _filepath is None:
print("No filepath for ", pid)
continue
com, com_idx = util.get_center_of_mass(util.DATA_FOLDER + _filepath)
val = {}
val['Name'] = str(pid) + "_" + str(_mm)
val['PositionGlobal'] = str(com[0]) + "," + str(com[1]) + "," + str(
com[2])
val['desc'] = str(_desc)
lobe = label_defs[lobes_brain[com_idx[0], com_idx[1], com_idx[2]]]
right_left = 'left' if com_idx[0] < 99 else 'right'
res_lobes_brain[lobe] = res_lobes_brain.get(lobe, [0, 0, 0])
res_right_left_brain[right_left] = res_right_left_brain.get(
right_left, [0, 0, 0])
print(right_left, lobe)
if _mm == 1:
res_lobes_brain[lobe][0] += 1
res_right_left_brain[right_left][0] += 1
image_ids_1.extend([_id])
elif _mm == 2:
res_lobes_brain[lobe][1] += 1
res_right_left_brain[right_left][1] += 1
image_ids_2.extend([_id])
elif _mm == 3:
res_lobes_brain[lobe][2] += 1
res_right_left_brain[right_left][2] += 1
image_ids_3.extend([_id])
image_ids.extend([_id])
print(pid, _mm)
patients += str(pid) + ': ' + str(_mm) + '\n'
if _mm == 1:
tag_data_1.append(val)
elif _mm == 2:
tag_data_2.append(val)
elif _mm == 3:
tag_data_3.append(val)
print(format_dict(res_lobes_brain))
lobes_brain_file = open(folder + "lobes_brain.txt", 'w')
lobes_brain_file.write(format_dict(res_lobes_brain))
lobes_brain_file.close()
lobes_brain_file = open(folder + "lobes_brain.txt", 'a')
lobes_brain_file.write(format_dict(res_right_left_brain))
lobes_brain_file.write(patients)
lobes_brain_file.close()
print(len(image_ids))
result = util.post_calculations(image_ids_1)
util.avg_calculation(result['all'],
'mm_1',
None,
True,
folder,
save_sum=True)
result = util.post_calculations(image_ids_2)
util.avg_calculation(result['all'],
'mm_2',
None,
True,
folder,
save_sum=True)
result = util.post_calculations(image_ids_3)
util.avg_calculation(result['all'],
'mm_3',
None,
True,
folder,
save_sum=True)
result = util.post_calculations(image_ids)
util.avg_calculation(result['all'],
'mm_1_2_3',
None,
True,
folder,
save_sum=True)
return
tag_data = {
"tag_data_1": tag_data_1,
"tag_data_2": tag_data_2,
"tag_data_3": tag_data_3
}
pickle.dump(tag_data, open("tag_data.pickle", "wb"))
cursor.close()
conn.close()
util.write_fcsv("mm_1", folder, tag_data_1, '1 0 0', 13)
util.write_fcsv("mm_2", folder, tag_data_2, '0 1 0', 5)
util.write_fcsv("mm_3", folder, tag_data_3, '0 0 1', 6)
result = util.post_calculations(image_ids)
util.avg_calculation(result['all'],
'all',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result['img'], 'img', None, True, folder)
def process(folder):
""" Post process data """
print(folder)
util.setup(folder)
image_ids = find_images("post")
if True:
import nibabel as nib
conn = sqlite3.connect(util.DB_PATH)
conn.text_factory = str
cursor = conn.execute(
'''SELECT pid from Patient where study_id = ? ''',
("LGG_reseksjonsgrad", ))
k = 0
for row in cursor:
k += 1
cursor2 = conn.execute(
'''SELECT id from Images where pid = ? and diag_pre_post = ?''',
(row[0], "post"))
ids = []
for _id in cursor2:
ids.append(_id[0])
cursor2.close()
if not ids:
continue
images_post = post_calculations(ids)
file_name_post = images_post['all'][0]
cursor2 = conn.execute(
'''SELECT id from Images where pid = ? and diag_pre_post = ?''',
(row[0], "pre"))
ids = []
for _id in cursor2:
ids.append(_id[0])
images_pre = util.post_calculations(ids)
file_name_pre = images_pre['all'][0]
img_pre = nib.load(file_name_pre)
img_post = nib.load(file_name_post)
temp = img_pre.get_data() - img_post.get_data()
temp = temp.flatten()
print(sum(temp < 0), file_name_pre, file_name_post)
cursor.close()
conn.close()
# return
result_post = post_calculations(image_ids)
print(len(result_post['all']))
util.avg_calculation(result_post['all'],
'all_post',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result_post['img'], 'img_post', None, True, folder)
image_ids = find_images("pre")
result_pre = util.post_calculations(image_ids)
print(len(result_pre['all']))
util.avg_calculation(result_pre['all'],
'all_pre',
None,
True,
folder,
save_sum=True)
util.avg_calculation(result_pre['img'], 'img_pre', None, True, folder)
util.calc_resection_prob(result_pre['all'], result_post['all'],
'resection_prob', True, folder, -1)
# util.setup("LGG_POST_RES/", "LGG")
# elif False:
# import do_img_registration_LGG_PRE as do_img_registration
# util.setup("LGG_PRE_RES/", "LGG")
# elif False:
# import do_img_registration_GBM as do_img_registration
# util.setup("GBM_RES2/", "GBM")
params = ['Index_value', 'Global_index', 'Mobility', 'Selfcare', 'Activity', 'Pain', 'Anxiety']
util.mkdir_p("LGG_GBM_RES")
FOLDER = "LGG_GBM_RES/" # "LGG_GBM_RES/GBM"
util.setup(FOLDER)
(image_ids, qol) = util.get_image_id_and_qol('Index_value')
print(image_ids, len(image_ids))
result = util.post_calculations(image_ids)
util.calculate_t_test(result['all'], 0.85)
for qol_param in params:
(image_ids, qol) = util.get_image_id_and_qol(qol_param)
print(image_ids)
result = util.post_calculations(image_ids)
for label in result:
print(label)
if label == 'img':
continue
util.avg_calculation(result[label], label + '_' + qol_param, qol, True, FOLDER)
util.avg_calculation(result['img'], label, None, True, FOLDER)
util.sum_calculation(result['img'], label, None, True, FOLDER)