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generate_patches.py
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generate_patches.py
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from patch_generation.transform2h5 import Transform2h5
import h5py
import os
def create_training_set():
nifti_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL'
roi_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL_liverSeg'
tumor_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL_onlytumors'
output_path = '/mnt/local/aszeskin/asher/liver_data'
output_filename = 'BL_all_patches.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 35
sampling_step = 2
transform = Transform2h5(nifti_dir_path, output_path,output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_nifti_patches()
check_file(output_path, output_filename)
def create_validation_set():
nifti_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allFU'
roi_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allFU_liverSeg/'
tumor_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allFU_newAndOldTumors/'
output_path = '/mnt/local/aszeskin/asher/liver_data'
output_filename = 'FU_patches.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_newTumors_copy'
patch_size = 35
sampling_step = 6
transform = Transform2h5(nifti_dir_path, output_path, output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_nifti_patches()
check_file(output_path, output_filename)
def create_training_and_validation_from_BL():
nifti_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL'
roi_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL_liverSeg'
tumor_dir_path = '/cs/labs/josko/aszeskin/Rafi_Tumor_data/allBL_onlytumors'
output_path = '/mnt/local/aszeskin/asher/liver_data/'
output_filename = 'patches.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 35
sampling_step = 3
transform = Transform2h5(nifti_dir_path, output_path,output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_patches_split_train_validation()
check_file(output_path, 'patches_train.h5')
check_file(output_path, 'patches_validation.h5')
def create_unet_training_set():
train_root = '/cs/labs/josko/asherp7/follow_up/train_combined_data'
nifti_dir_path = os.path.join(train_root, 'ct_scans')
roi_dir_path = os.path.join(train_root, 'liver_seg')
tumor_dir_path = os.path.join(train_root, 'tumors')
output_path = '/mnt/local/aszeskin/asher/liver_data/unet_26_3'
output_filename = 'unet_train.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 96
sampling_step = 3
save_tumor_segmentation = True
transform = Transform2h5(nifti_dir_path, output_path,output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix,
save_tumor_segmentation=save_tumor_segmentation)
transform.save_all_nifti_patches()
check_file(output_path, output_filename)
def create_unet_validation_set():
validation_root = '/cs/labs/josko/asherp7/follow_up/validation_combined_data'
nifti_dir_path = os.path.join(validation_root, 'ct_scans')
roi_dir_path = os.path.join(validation_root, 'liver_seg')
tumor_dir_path = os.path.join(validation_root, 'tumors')
output_path = '/mnt/local/aszeskin/asher/liver_data/unet_26_3'
output_filename = 'unet_validation.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 96
sampling_step = 3
save_tumor_segmentation = True
transform = Transform2h5(nifti_dir_path, output_path, output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix,
save_tumor_segmentation=save_tumor_segmentation)
transform.save_all_nifti_patches()
check_file(output_path, output_filename, check_mask_patches=True)
def create_training_set_and_validation_set_combined_data():
data_root = '/cs/labs/josko/asherp7/follow_up/data_3_4_2020/'
nifti_dir_path = os.path.join(data_root, 'ct_scans')
roi_dir_path = os.path.join(data_root, 'liver_seg')
tumor_dir_path = os.path.join(data_root, 'tumors')
output_path = '/mnt/local/aszeskin/asher/liver_data'
output_filename = 'combined_105_ct_patches_step_3.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 35
sampling_step = 3
validation_ratio = 0.2
transform = Transform2h5(nifti_dir_path, output_path, output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_patches_split_train_validation(validation_ratio)
check_file(output_path, 'combined_patches_step_3_train.h5')
check_file(output_path, 'combined_patches_step_3_validation.h5')
def create_training_set_combined_data():
data_root = '/cs/labs/josko/asherp7/follow_up/combined_data/training'
nifti_dir_path = os.path.join(data_root, 'ct_scans')
roi_dir_path = os.path.join(data_root, 'liver_seg')
tumor_dir_path = os.path.join(data_root, 'tumors')
output_path = '/mnt/local/aszeskin/asher/liver_data'
output_filename = 'combined_step_2_training.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 35
sampling_step = 2
transform = Transform2h5(nifti_dir_path, output_path,output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_nifti_patches()
check_file(output_path, output_filename)
def create_validation_set_combined_data():
data_root = '/cs/labs/josko/asherp7/follow_up/combined_data/validation'
nifti_dir_path = os.path.join(data_root, 'ct_scans')
roi_dir_path = os.path.join(data_root, 'liver_seg')
tumor_dir_path = os.path.join(data_root, 'tumors')
output_path = '/mnt/local/aszeskin/asher/liver_data'
output_filename = 'combined_step_2_validation.h5'
roi_suffix = '_liverseg'
tumor_suffix = '_Tumors'
patch_size = 35
sampling_step = 2
transform = Transform2h5(nifti_dir_path, output_path,output_filename, ('L', 'P', 'S'), patch_size, sampling_step,
roi_dir_path, roi_suffix, tumor_dir_path, tumor_suffix)
transform.save_all_nifti_patches()
check_file(output_path, output_filename)
def check_file(output_path, file_name, check_mask_patches=False):
file_path = os.path.join(output_path, file_name)
f = h5py.File(file_path, 'r')
print('\ndatasets in ', file_path, ':\n')
print(f.keys())
print(f['patches'])
print(f['labels'])
print(f['tumor_idx'])
print(f['non_tumor_idx'])
if check_mask_patches:
print(f['mask_patches'])
if __name__ == '__main__':
# create_training_set()
# create_validation_set()
# create_training_and_validation_from_BL()
# create_unet_training_set()
# create_unet_validation_set()
create_training_set_and_validation_set_combined_data()
# create_training_set_combined_data()
# create_validation_set_combined_data()