File size: 8,896 Bytes
da9fb1e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | import os
import glob
import pandas as pd
import SimpleITK as sitk
import argparse
import json
from tqdm import tqdm
from util import meta_data
import util
# from bert_helper import *
# model_name = "bert-large-uncased"
# reduce_method = 'mean'
# max_words_num = 32 # max number of words in the caption > 2
# embeder, tokenizer = get_frozen_embeder(model_name)
# string1 = "modality: ct, gender: female, age: 51, roi: abdomen"
# embeder_output1 = str2emb(string1, max_words_num, embeder, tokenizer, reduce_method=reduce_method)
# string2 = "modality: ct, gender: female, age: 50, roi: head"
# embeder_output2 = str2emb(string2, max_words_num, embeder, tokenizer, reduce_method=reduce_method)
# input_size = embeder.config.vocab_size
# in_size = embeder.config.hidden_size
# print(embeder, input_size, in_size)
# print(tokenizer)
# print(embeder_output1)
# print(embeder_output1.shape) # torch.Size([1, 8, 768])
# print(embeder_output2)
# print(embeder_output2.shape) # torch.Size([1, 8, 768])
# error = torch.abs(embeder_output1 - embeder_output2)
# print(error)
# print("Embedding distance between the two sentences: ")
# print(f"String1: {string1}")
# print(f"String2: {string2}")
# print(torch.mean(error))
# exit()
CLAMP_RANGE_CT = [-300,300]
CLAMP_RANGE_MRI = [-1,0] # MRI images threshold placeholder TBC...
# def find_metadata_files(path):
# # for Cancer Image Archive (TCIA) dataset
# search_pattern = os.path.join(path, '**', 'metadata.csv')
# return glob.glob(search_pattern, recursive=True)
def find_metadata_files(path):
# for Cancer Image Archive (TCIA) dataset
search_pattern = os.path.join(path, '*.csv')
return glob.glob(search_pattern, recursive=True)
##added by yanguoqing on 20250527
def find_image_dirs(path):
return os.listdir(path)
##modify by yanguoqing on 20250527
def load_dicom_images(folder_path):
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(folder_path)
reader.SetFileNames(dicom_names)
image = reader.Execute()
return dicom_names,image
##added by yanguoqing on 20250527
def load_dicom_tag(imgs):
reader = sitk.ImageFileReader()
# dicom_names = reader.GetGDCMSeriesFileNames(folder_path)
reader.SetFileName(imgs)
reader.ReadImageInformation() # 仅读取元信息，不加载像素数据
# metadata_keys = reader.GetMetaDataKeys()
tag=reader.Execute()
return tag
def save_nifti(image, output_path, folder_path):
# Set metadata in the NIfTI file's header
output_dirpath = os.path.dirname(output_path)
if not os.path.exists(output_dirpath):
print(f"Creating directory {output_dirpath}")
os.makedirs(output_dirpath)
# Set metadata in the NIfTI file's header
image.SetMetaData("FolderPath", folder_path)
sitk.WriteImage(image, output_path)
##modify by yanguoqing on 20250527
def convert_windows_to_linux_path(windows_path):
# Replace backslashes with forward slashes and remove the drive letter
# Some meta files have windows paths, but the data is stored on a linux server
linux_path = windows_path.replace('\\', '/')
if ':' in linux_path:
linux_path = linux_path.split(':', 1)[1]
return linux_path
def main(target_path, output_dir):
metadata_files = find_metadata_files(target_path)
pid_dirs=find_image_dirs(target_path)
failed_files = []
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
json_output_path = os.path.join(output_dir, 'nifti_mappings.json')
failed_files_path = os.path.join(output_dir, 'failed_files.json')
meta = meta_data()
# Initialize the JSON file
if not os.path.exists(json_output_path):
with open(json_output_path, 'w') as json_file:
json.dump({}, json_file)
if pid_dirs:
for pid_dir in tqdm(pid_dirs, desc="Processing pid dirs"):
if not os.path.isdir(os.path.join(target_path,pid_dir)):
continue
meta_file=os.path.join(target_path,'%s.csv'%pid_dir)
if os.path.isfile(meta_file):
mf_flag=True
else:
mf_flag=False
image_dirs=find_image_dirs(os.path.join(target_path,pid_dir))
for data_dir in tqdm(image_dirs, desc="Processing images files"):
location=data_dir
full_path=os.path.join(target_path,pid_dir,data_dir)
# full_path = convert_windows_to_linux_path(full_path)
if not os.path.isdir(full_path):
continue
try:
print(full_path)
dicom_fp,dicom_image = load_dicom_images(full_path)
spacing_info = dicom_image.GetSpacing()
metadata_keys = dicom_image.GetMetaDataKeys()
dtag=load_dicom_tag(dicom_fp[0])
uid=dtag.GetMetaData('0020|000e') ##Series Instance UID
modality=dtag.GetMetaData('0008|0060')##Modality
study='OSIC_PFP'##Dataset_name
CIA_other_info = {
'Study_UID':uid,
'metadata_file':''
# 'Series_Description':serise_desc
}
if mf_flag:
CIA_other_info['metadata_file']=meta_file
size = list(dicom_image.GetSize())
resampler =util.get_unisize_resampler(dicom_image, interpolator='linear', spacing=spacing_info, size=size)
# resize the image
if resampler is not None:
proces_image = resampler.Execute(dicom_image)
CIA_other_info['Resample'] = True
else:
proces_image = dicom_image
CIA_other_info['Resample'] = False
# threshold the image
if 'CT' in modality:
proces_image = util.clamp_image(proces_image, CLAMP_RANGE_CT)
else:
pass
except RuntimeError:
failed_files.append(full_path)
print(f"Failed to load DICOM images from {full_path}")
continue
meta.add_keyvalue('Spacing_mm',min(spacing_info))
meta.add_keyvalue('OriImg_path',full_path)
meta.add_keyvalue('Size',size)
meta.add_keyvalue('Modality',modality)
meta.add_keyvalue('Dataset_name',study)
meta.add_keyvalue('ROI','lung')
meta.add_extra_keyvalue('Metadata',CIA_other_info)
output_path = os.path.join(output_dir,pid_dir, f"{os.path.basename(full_path)}.nii.gz")
# output_path=convert_windows_to_linux_path(output_path)
save_nifti(proces_image, output_path, full_path)
print(f"Saved NIfTI file to {output_path}")
# Write the mapping to the JSON file on the fly
with open(json_output_path, 'r+') as json_file:
existing_mappings = json.load(json_file)
existing_mappings[output_path] = meta.get_meta_data()
json_file.seek(0)
json.dump(existing_mappings, json_file, indent=4)
json_file.truncate()
else:
print("No metadata.csv files found.")
with open(failed_files_path, "w") as json_file:
json.dump(failed_files, json_file)
print(f"The list has been written to {failed_files_path}")
print(f"Saved NIfTI mappings to {json_output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process DICOM files and save as NIfTI.")
parser.add_argument("--target_path", type=str, help="Path to the target directory containing metadata files.", default="/home/data/Github/data/data_gen_def/DATASETS/Kaggle/osic_pulmonary_fibrosis_progression_Segmentation/osic-pulmonary-fibrosis-progression")
parser.add_argument("--output_dir", type=str, help="Directory to save the NIfTI files.", default="/home/data/Github/data/data_gen_def/DATASETS_processed/Kaggle_osic/")
args = parser.parse_args()
print(args.target_path, args.output_dir)
main(args.target_path, args.output_dir)
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