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data_input.py
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data_input.py
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# Data downloaded from:
# https://chaos.grand-challenge.org/Combined_Healthy_Abdominal_Organ_Segmentation/
from utils import get_data_root
def get_data_path(data_root=None):
if data_root is None:
data_root = get_data_root()
data_path = data_root + 'Train_Sets/'
return data_path
def get_definition_file_name(data_root=None):
definition_file_name = get_data_path(data_root) + 'definitions.txt'
return definition_file_name
def get_patient_indices(data_root=None,
verbose=True):
definition_file_name = get_definition_file_name(data_root)
with open(definition_file_name, 'r') as f:
lines = f.readlines()
patient_indices_as_str = lines[1]
patient_indices = [int(patient_no)
for patient_no in patient_indices_as_str.split(',')]
if verbose:
print('{} patients found: {}'.format(
len(patient_indices),
patient_indices
))
return patient_indices
def get_patient_data_path(patient_no,
data_root=None):
patient_data_path = get_data_path(data_root) + 'CT/' + str(patient_no) + '/'
return patient_data_path
def get_path_to_image(patient_no,
data_root=None):
path_to_image = get_patient_data_path(patient_no, data_root) + '/DICOM_anon/'
return path_to_image
def get_path_to_ground_truth(patient_no,
data_root=None):
path_to_ground_truth = get_patient_data_path(patient_no, data_root) + 'Ground/'
return path_to_ground_truth
def main():
return True
if __name__ == '__main__':
main()