def process_labels2(folder):
""" Post process data tumor volume"""
print(folder)
util.setup(folder)
conn = sqlite3.connect(util.DB_PATH, timeout=120)
conn.text_factory = str
cursor = conn.execute('''SELECT pid from Patient where study_id = ?''', ("qol_grade3,4", ))
atlas_path = "/home/dahoiv/disk/Dropbox/Jobb/gbm/Atlas/Hammers/Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12.nii.gz"
resample = slicer.registration.brainsresample.BRAINSResample(command=util.BRAINSResample_PATH,
inputVolume=atlas_path,
outputVolume=os.path.abspath(folder +
'Hammers_mith-n30r95-MaxProbMap-full'
'-MNI152-SPM12_resample.nii.gz'),
referenceVolume=os.path.abspath(util.TEMPLATE_VOLUME))
resample.run()
img = nib.load(folder + 'Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz')
lobes_brain = img.get_data()
label_defs = util.get_label_defs_hammers_mith()
res_lobes_brain = {}
book = Workbook()
sheet = book.active
sheet.cell(row=1, column=1).value = 'PID'
sheet.cell(row=1, column=2).value = 'Lobe'
# sheet.cell(row=1, column=3).value = 'Center of mass'
k = 2
for pid in cursor:
pid = pid[0]
_id = conn.execute('''SELECT id from Images where pid = ?''', (pid, )).fetchone()
if not _id:
print("---No data for ", pid)
continue
_id = _id[0]
_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)
lobe = label_defs.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]], 'other')
res_lobes_brain[pid] = lobe
sheet.cell(row=k, column=1).value = pid
sheet.cell(row=k, column=2).value = lobe
sheet.cell(row=k, column=4).value = str(com_idx[0]) + " " + str(com_idx[1]) + " " + str(com_idx[2])
k += 1
book.save("brain_lobes_Hammers_mith_n30r95.xlsx")
print(res_lobes_brain, len(res_lobes_brain))
def process_labels(folder):
""" Post process data tumor volume"""
print(folder)
util.setup(folder)
conn = sqlite3.connect(util.DB_PATH, timeout=120)
conn.text_factory = str
cursor = conn.execute('''SELECT pid from Patient where study_id = ?''', ("qol_grade3,4", ))
img = nib.load("/home/dahoiv/disk/data/MolekylareMarkorer/lobes_brain.nii")
lobes_brain = img.get_data()
label_defs = util.get_bigger_label_defs()
label_defs_r_l = util.get_right_left_label_defs()
res_lobes_brain = {}
book = Workbook()
sheet = book.active
sheet.cell(row=1, column=1).value = 'PID'
sheet.cell(row=1, column=2).value = 'Lobe'
sheet.cell(row=1, column=3).value = 'Right/Left'
# sheet.cell(row=1, column=3).value = 'Center of mass'
k = 2
for pid in cursor:
pid = pid[0]
_id = conn.execute('''SELECT id from Images where pid = ?''', (pid, )).fetchone()
if not _id:
print("---No data for ", pid)
continue
_id = _id[0]
_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)
lobe = label_defs.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]], 'other')
res_lobes_brain[pid] = lobe
sheet.cell(row=k, column=1).value = pid
sheet.cell(row=k, column=2).value = lobe
sheet.cell(row=k, column=3).value = 'left' if com_idx[0] < 99 else 'right'
rl = label_defs_r_l.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]], 'other')
if rl != 'unknown' and rl != sheet.cell(row=k, column=3).value:
print("\n\n\n", pid)
# sheet.cell(row=k, column=3).value = str(com[0]) + " " + str(com[1]) + " " + str(com[2])
# sheet.cell(row=k, column=4).value = str(com_idx[0]) + " " + str(com_idx[1]) + " " + str(com_idx[2])
k += 1
book.save("brain_lobes.xlsx")
print(res_lobes_brain, len(res_lobes_brain))
def find_bad_registration():
util.setup(folder, "meningiomer")
image_ids = find_images()
template_mask = nib.load(util.TEMPLATE_MASK).get_data()
conn = sqlite3.connect(util.DB_PATH, timeout=120)
k = 0
exclude = []
for _id in image_ids:
_filepath = conn.execute(
"SELECT filepath_reg from Labels where image_id = ?",
(_id, )).fetchone()[0]
com, com_idx = util.get_center_of_mass(util.DATA_FOLDER + _filepath)
if template_mask[com_idx[0], com_idx[1], com_idx[2]] == 0:
print(_filepath)
k += 1
exclude.append(_id)
print(k)
return exclude
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_labels(folder, pids_to_exclude=()):
""" Post process data tumor volume"""
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''')
atlas_path = util.ATLAS_FOLDER_PATH + 'Hammers/Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12.nii.gz'
atlas_resampled_path = folder + 'Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz'
resample = brainsresample.BRAINSResample(
command=util.BRAINSResample_PATH,
inputVolume=atlas_path,
outputVolume=os.path.abspath(atlas_resampled_path),
referenceVolume=os.path.abspath(util.TEMPLATE_VOLUME))
resample.run()
img = nib.load(atlas_resampled_path)
lobes_brain = img.get_data()
label_defs = util.get_label_defs_hammers_mith()
res_lobes_brain = {}
coordinates_svz = util.get_label_coordinates(util.ATLAS_FOLDER_PATH +
'SubventricularZone2.nii.gz')
surface_dg = util.get_surface(util.ATLAS_FOLDER_PATH +
'DentateGyrus.nii.gz')
book = Workbook()
sheet = book.active
sheet.cell(row=1, column=1).value = 'PID'
sheet.cell(row=1, column=2).value = 'MM'
sheet.cell(row=1, column=3).value = 'Lobe, center of tumor'
sheet.cell(row=1,
column=4).value = 'Distance from SVZ to center of tumor (mm)'
sheet.cell(row=1,
column=5).value = 'Distance from SVZ to border of tumor (mm)'
sheet.cell(row=1,
column=6).value = 'Distance from DG to center of tumor (mm)'
sheet.cell(row=1,
column=7).value = 'Distance from DG to border of tumor (mm)'
sheet.cell(row=1, column=8).value = 'Tumor volume (mm^3)'
i = 8
label_defs_to_column = {}
for key in label_defs:
i += 1
sheet.cell(row=1, column=i).value = label_defs[key]
label_defs_to_column[key] = i
k = 2
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]
_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)
surface = util.get_surface(util.DATA_FOLDER + _filepath)
print(pid, com_idx)
dist_from_svz_to_com = distance.cdist(coordinates_svz, [com],
'euclidean').min()
dist_from_svz_to_border = distance.cdist(coordinates_svz,
surface['point_cloud'],
'euclidean').min()
dist_from_dg_to_com = util.get_min_distance(surface_dg, [com])
dist_from_dg_to_border = util.get_min_distance(surface_dg,
surface['point_cloud'])
lobe = label_defs.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]],
'other')
res_lobes_brain[pid] = lobe
img = nib.load(util.DATA_FOLDER + _filepath)
tumor_data = img.get_data()
voxel_size = img.header.get_zooms()
voxel_volume = np.prod(voxel_size[0:3])
n_voxels = (tumor_data > 0).sum()
tumor_volume = n_voxels * voxel_volume
union_data = lobes_brain * tumor_data
union_data = union_data.flatten()
lobe_overlap = ''
for column in range(1, 1 + max(label_defs_to_column.values())):
sheet.cell(row=k, column=column).value = 0
for _lobe in np.unique(union_data):
column = label_defs_to_column.get(_lobe)
if column is None:
continue
sheet.cell(row=k, column=column).value = 1
lobe_overlap += label_defs.get(_lobe, '') + ', '
_mm = conn.execute(
"SELECT Subgroup from MolekylareMarkorer where pid = ?",
(pid, )).fetchone()[0]
sheet.cell(row=k, column=1).value = pid
sheet.cell(row=k, column=2).value = str(_mm)
sheet.cell(row=k, column=3).value = lobe
sheet.cell(row=k, column=4).value = round(dist_from_svz_to_com, 2)
sheet.cell(row=k, column=5).value = round(dist_from_svz_to_border, 2)
sheet.cell(row=k, column=6).value = round(dist_from_dg_to_com, 2)
sheet.cell(row=k, column=7).value = round(dist_from_dg_to_border, 2)
sheet.cell(row=k, column=8).value = round(tumor_volume, 1)
k += 1
book.save(folder + "brain_lobes_Hammers_mith_n30r95.xlsx")
print(res_lobes_brain, len(res_lobes_brain))
import nibabel as nib
import util
from scipy.spatial import distance
import numpy as np
atlas_path = "Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz"
label = 49
c, ci = util.get_center_of_mass(atlas_path)
c_label, ci_label = util.get_center_of_mass(atlas_path, 49)
#coordinates1 = util.get_label_coordinates(atlas_path,22)
#coordinates2 = util.get_label_coordinates(atlas_path,23)
print('Done 1')
#dist = distance.cdist(coordinates1,coordinates2,'euclidean')
print('Done 2')
#print(np.amin(dist))
print('Done 3')
coords1 = [(5, 14, 6), (3, 9, 16), (18, 2, 15), (9, 1, 1)]
coords1 = [(5, 14, 6), (3, 9, 16), (18, 2, 15), (9, 1, 1)]
coords2 = [(3, 6, 8), (6, 9, 1)]
dist1 = distance.cdist(coords1, coords1, 'euclidean')
print(c_label)
print(distance.cdist(coords1, [c_label], 'euclidean'))
def process_labels(folder):
""" Post process data tumor volume"""
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''')
atlas_path = "/home/dahoiv/disk/Dropbox/Jobb/gbm/Atlas/Hammers/Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12.nii.gz"
resample = slicer.registration.brainsresample.BRAINSResample(
command=BRAINSResample_PATH,
inputVolume=atlas_path,
outputVolume=os.path.abspath(folder +
'Hammers_mith-n30r95-MaxProbMap-full'
'-MNI152-SPM12_resample.nii.gz'),
referenceVolume=os.path.abspath(util.TEMPLATE_VOLUME))
resample.run()
img = nib.load(
folder +
'Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz')
lobes_brain = img.get_data()
label_defs = util.get_label_defs_hammers_mith()
res_lobes_brain = {}
book = Workbook()
sheet = book.active
sheet.cell(row=1, column=1).value = 'PID'
sheet.cell(row=1, column=3).value = 'MM'
sheet.cell(row=1, column=3).value = 'Lobe, center of tumor'
i = 3
label_defs_to_column = {}
for key in label_defs:
i += 1
sheet.cell(row=1, column=i).value = label_defs[key]
label_defs_to_column[key] = i
# sheet.cell(row=1, column=3).value = 'Center of mass'
k = 2
for pid in cursor:
pid = pid[0]
_id = conn.execute('''SELECT id from Images where pid = ?''',
(pid, )).fetchone()
if not _id:
print("---No data for ", pid)
continue
_id = _id[0]
_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)
print(pid, com_idx)
lobe = label_defs.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]],
'other')
res_lobes_brain[pid] = lobe
tumor_data = nib.load(util.DATA_FOLDER + _filepath).get_data()
union_data = lobes_brain * tumor_data
union_data = union_data.flatten()
lobe_overlap = ''
for column in range(1, 1 + max(label_defs_to_column.values())):
sheet.cell(row=k, column=column).value = 0
for _lobe in np.unique(union_data):
column = label_defs_to_column.get(_lobe)
if column is None:
continue
sheet.cell(row=k, column=column).value = 1
lobe_overlap += label_defs.get(_lobe, '') + ', '
_mm = conn.execute(
"SELECT Subgroup from MolekylareMarkorer where pid = ?",
(pid, )).fetchone()[0]
sheet.cell(row=k, column=1).value = pid
sheet.cell(row=k, column=2).value = str(_mm)
sheet.cell(row=k, column=3).value = lobe
k += 1
book.save(folder + "brain_lobes_Hammers_mith_n30r95.xlsx")
print(res_lobes_brain, len(res_lobes_brain))
def process_labels(folder, pids_to_exclude=None):
""" Create Excel-document with overview of which brain areas are affected by the tumors"""
print(folder)
util.setup(folder)
conn = sqlite3.connect(util.DB_PATH, timeout=120)
conn.text_factory = str
pids, image_ids = util.get_pids_and_image_ids(study_id="GBM_survival_time",exclude_pid=pids_to_exclude)
atlas_path = util.ATLAS_FOLDER_PATH + "Hammers/Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12.nii.gz"
atlas_resampled_path = folder + 'Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz'
resample = brainsresample.BRAINSResample(command=util.BRAINSResample_PATH,
inputVolume=atlas_path,
outputVolume=os.path.abspath(atlas_resampled_path),
referenceVolume=os.path.abspath(util.TEMPLATE_VOLUME))
resample.run()
img = nib.load(atlas_resampled_path)
lobes_brain = img.get_data()
label_defs = util.get_label_defs_hammers_mith()
res_lobes_brain = {}
ventricle_label = 49
com_ventricle, com_idx_ventricle = util.get_center_of_mass(folder + 'Hammers_mith-n30r95-MaxProbMap-full-MNI152-SPM12_resample.nii.gz',ventricle_label)
book = Workbook()
sheet = book.active
sheet.cell(row=1, column=1).value = 'PID'
sheet.cell(row=1, column=2).value = 'Lobe, center of tumor'
sheet.cell(row=1, column=3).value = 'Distance from center of 3rd ventricle to center of tumor (mm)'
sheet.cell(row=1, column=4).value = 'Distance from center of 3rd ventricle to border of tumor (mm)'
i = 4
label_defs_to_column = {}
for key in label_defs:
i += 1
sheet.cell(row=1, column=i).value = label_defs[key]
label_defs_to_column[key] = i
# sheet.cell(row=1, column=3).value = 'Center of mass'
k = 2
for (pid, _id) in zip(pids, image_ids):
_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)
surface = util.get_surface(util.DATA_FOLDER + _filepath)
print(pid, com_idx)
dist_from_ventricle_to_com = np.linalg.norm(np.array(com)-np.array(com_ventricle))
dist_from_ventricle_to_border = util.get_min_distance(surface, [com_ventricle])
lobe = label_defs.get(lobes_brain[com_idx[0], com_idx[1], com_idx[2]], 'other')
res_lobes_brain[pid] = lobe
tumor_data = nib.load(util.DATA_FOLDER + _filepath).get_data()
union_data = lobes_brain * tumor_data
union_data = union_data.flatten()
lobe_overlap = ''
for column in range(1, 1+max(label_defs_to_column.values())):
sheet.cell(row=k, column=column).value = 0
for _lobe in np.unique(union_data):
column = label_defs_to_column.get(_lobe)
if column is None:
continue
sheet.cell(row=k, column=column).value = 1
lobe_overlap += label_defs.get(_lobe, '') + ', '
sheet.cell(row=k, column=1).value = pid
sheet.cell(row=k, column=2).value = lobe
sheet.cell(row=k, column=3).value = round(dist_from_ventricle_to_com,2)
sheet.cell(row=k, column=4).value = round(dist_from_ventricle_to_border,2)
k += 1
book.save(folder + "brain_lobes_Hammers_mith_n30r95.xlsx")
print(res_lobes_brain, len(res_lobes_brain))