def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('in_file')
args = arg_parser.parse_args(args)
print(args)
p = pydicom.read_file(args['in_file'])
pixels = p.pixel_array
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
args = arg_parser.parse_args(args)
os.makedirs(args['out_dir'], exist_ok=True)
print('Calculate ratios')
fp = os.path.join(args['src_dir'], 'features_all.csv')
df = pd.read_csv(fp, index_col='SID')
tot_area = \
df['muscle_original_shape_SurfaceArea'] + \
df['SAT_original_shape_SurfaceArea'] + \
df['VAT_original_shape_SurfaceArea']
mus_area = df['muscle_original_shape_SurfaceArea']
vat_area = df['VAT_original_shape_SurfaceArea']
sat_area = df['SAT_original_shape_SurfaceArea']
fat_area = \
df['SAT_original_shape_SurfaceArea'] + \
df['VAT_original_shape_SurfaceArea']
columns = {
'mus_tot': mus_area / tot_area,
'vat_tot': vat_area / tot_area,
'sat_tot': sat_area / tot_area,
'fat_tot': fat_area / tot_area,
'sat_vat': sat_area / vat_area,
'mus_vat': mus_area / vat_area,
'mus_sat': mus_area / sat_area,
'mus_fat': mus_area / fat_area,
}
df = pd.DataFrame()
df = df.from_dict(
{
'mus_tot': columns['mus_tot'],
'vat_tot': columns['vat_tot'],
'sat_tot': columns['sat_tot'],
'fat_tot': columns['fat_tot'],
'sat_vat': columns['sat_vat'],
'mus_vat': columns['mus_vat'],
'mus_sat': columns['mus_sat'],
'mus_fat': columns['mus_fat'],
},
orient='columns')
fp = os.path.join(args['src_dir'], 'features_area_ratios.csv')
df.to_csv(fp, index=True)
print(df.shape)
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
args = arg_parser.parse_args(args)
data = {}
# For each patient ID, load file path
with open('{}/pat_id_file.csv'.format(args['src_dir']), 'r') as f:
header = True
for line in f.readlines():
if header:
header = False
continue
if line.startswith('#'):
continue
items = line.strip().split(',')
data[items[0]] = [items[1]]
# For each patient ID, load random ID
with open('{}/pat_id_random_id.csv'.format(args['src_dir']), 'r') as f:
header = True
for line in f.readlines():
if header:
header = False
continue
if line.startswith('#'):
continue
items = line.strip().split(',')
data[items[0]].append(items[1])
os.makedirs(args['out_dir'], exist_ok=True)
# Copy files to randomized new name
for pat_id in data.keys():
dcm_file_path = data[pat_id][0]
tag_file_path = get_tag_file_path(dcm_file_path)
random_id = data[pat_id][1]
dcm_file_name = replace_with_random_id(dcm_file_path, random_id)
tag_file_name = replace_with_random_id(tag_file_path, random_id)
shutil.copyfile(dcm_file_path,
os.path.join(args['out_dir'], dcm_file_name))
shutil.copyfile(tag_file_path,
os.path.join(args['out_dir'], tag_file_name))
print('Renamed {}'.format(dcm_file_path))
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('ctp_dir')
arg_parser.add_arg('tag_dir')
arg_parser.add_arg('out_dir')
arg_parser.add_arg('id_file')
args = arg_parser.parse_args(args)
random_ids = []
with open(args['id_file'], 'r') as f:
header = True
for line in f.readlines():
if header:
header = False
continue
random_ids.append(line.strip())
os.makedirs(args['out_dir'], exist_ok=True)
for random_id in random_ids:
file_path = get_file_path(os.path.join(args['ctp_dir'], random_id + '_dcm'))
shutil.copyfile(
file_path,
os.path.join(args['out_dir'], random_id + '.dcm'))
shutil.copyfile(
os.path.join(args['tag_dir'], random_id + '.tag'),
os.path.join(args['out_dir'], random_id + '.tag'))
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_file')
arg_parser.add_arg('out_file')
args = arg_parser.parse_args(args)
print(args)
lines = []
with open(args['src_file'], 'r') as f:
for line in f.readlines():
line = line.strip()
ix = line.split(',')[0]
fp = line.split(',')[1]
fn = os.path.split(fp)[1]
lines.append([ix, fn])
with open(args['out_file'], 'w') as f:
for line in lines:
f.write('{},{}\n'.format(line[0], line[1]))
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
args = arg_parser.parse_args(args)
os.makedirs(args['out_dir'], exist_ok=True)
print('Create feature file per tissue')
for tissue in ['muscle', 'SAT', 'VAT']:
body = []
index = []
header = None
for f in os.listdir(args['src_dir']):
expression = '_tag_{}.txt'.format(tissue)
if f.endswith(expression):
sid = os.path.splitext(f)[0][:-(len(expression) - 4)]
index.append(sid)
feature_file = os.path.join(args['src_dir'], f)
row, cols = get_row(feature_file)
if not header:
header = cols
body.append(row)
fp = os.path.join(args['out_dir'], 'features_{}.csv'.format(tissue))
df = pd.DataFrame(data=body, columns=header, index=index)
df.index.name = 'SID'
df.to_csv(fp, index=True)
# print(fp)
print('Creating feature file for ALL tissues')
dfs = []
for tissue in ['muscle', 'SAT', 'VAT']:
fp = os.path.join(args['out_dir'], 'features_{}.csv'.format(tissue))
df = pd.read_csv(fp, index_col='SID')
df.columns = ['{}_'.format(tissue) + c for c in df.columns]
dfs.append(df)
fp = os.path.join(args['out_dir'], 'features_all.csv')
df = pd.concat(dfs, axis=1)
df.index.name = 'SID'
df.to_csv(fp, index=True)
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
args = arg_parser.parse_args(args)
for f in os.listdir(args['src_dir']):
if f.endswith('.dcm'):
sid = os.path.splitext(f)[0]
tag_file = os.path.join(args['src_dir'], sid + '.tag')
dcm_file = os.path.join(args['src_dir'], sid + '.dcm')
# Convert TAG file to DICOM
tag2dcm = Tag2Dcm()
tag2dcm.set_input('tag_file', tag_file)
tag2dcm.set_input('dcm_file', dcm_file)
tag2dcm.set_param('out_dir', args['out_dir'])
out_file = tag2dcm.get_output('out_file')
# Copy DICOM to output directory as well
shutil.copyfile(dcm_file, os.path.join(args['out_dir'], sid + '.dcm'))
print(out_file)
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
args = arg_parser.parse_args(args)
print(args)
for tissue in ['muscle', 'SAT', 'VAT']:
for f in os.listdir(args['src_dir']):
expression = '_tag_{}.dcm'.format(tissue)
if f.endswith(expression):
sid = os.path.splitext(f)[0][:-(len(expression) - 4)]
in_file = os.path.join(args['src_dir'], sid + '.dcm')
mask_file = os.path.join(args['src_dir'],
sid + '_tag_{}.dcm'.format(tissue))
crf = CalculateRadiomicsFeatures()
crf.set_input('in_file', in_file)
crf.set_input('mask_file', mask_file)
crf.set_param('out_dir', args['out_dir'])
out_file = crf.get_output('out_file')
print(out_file)
def run(args):
arg_parser = ArgParser()
arg_parser.add_arg('src_dir')
arg_parser.add_arg('out_dir')
arg_parser.add_arg('label_map')
args = arg_parser.parse_args(args)
print(args)
for f in os.listdir(args['src_dir']):
if f.endswith('_tag.dcm'):
sid = os.path.splitext(f)[0][:-4]
tag_file = os.path.join(args['src_dir'], f)
dcm_file = os.path.join(args['src_dir'], sid + '.dcm')
dcm2masks = Dcm2Masks()
dcm2masks.set_input('dcm_file', tag_file)
dcm2masks.set_param('label_map', args['label_map'])
dcm2masks.set_param('out_dir', args['out_dir'])
out_files = dcm2masks.get_output('out_files')
# Copy DICOM to output directory as well
shutil.copyfile(dcm_file,
os.path.join(args['out_dir'], sid + '.dcm'))
print(out_files)