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, 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))
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))