# import os # import SimpleITK as sitk # import difflib # # 定義資料夾路徑 # T1C_folder_path = '/home/onlylian/sorted_OAR_files/T1C' # CTCTC_folder_path = '/home/onlylian/sorted_OAR_files/CT+CTC' # T1C_output_folder_path = '/home/onlylian/T1C_label_folder' # CTCTC_output_folder_path = '/home/onlylian/CT+CTC_label_folder' # # 定義組織標籤對應關係 # labels = { # "background": "0", # "Brainstem": "1", # "Right_Eye": "2", # "Left_Eye": "3", # "Optic_Chiasm": "4", # "Right_Optic_Nerve": "5", # "Left_Optic_Nerve": "6" # } # # 確保輸出資料夾存在 # os.makedirs(T1C_output_folder_path, exist_ok=True) # os.makedirs(CTCTC_output_folder_path, exist_ok=True) # def resample_image(image, reference_image): # resample = sitk.ResampleImageFilter() # resample.SetReferenceImage(reference_image) # resample.SetInterpolator(sitk.sitkNearestNeighbor) # resample.SetDefaultPixelValue(0) # return resample.Execute(image) # def process_images(input_folder, output_folder, image_type): # # 獲取所有病人的資料夾路徑 # patient_folders = sorted([os.path.join(dp, d) for dp, dn, filenames in os.walk(input_folder) for d in dn]) # file_counter = 1 # for patient_folder in patient_folders: # # 創建空的影像，用來合成所有器官 # combined_image = None # # 獲取病人文件夾中的所有文件名，並轉換為小寫形式 # patient_files = {f.lower(): f for f in os.listdir(patient_folder)} # filename = patient_folder.split('/')[-2] # # 遍歷每個病人的所有影像文件 # for organ_name, label in labels.items(): # organ_filename = f'struct_{organ_name}.nii.gz'.lower() # # 使用 difflib.get_close_matches 查找相似文件名 # possible_matches = difflib.get_close_matches(organ_filename, patient_files.keys(), n=1, cutoff=0.8) # if possible_matches: # matched_filename = patient_files[possible_matches[0]] # organ_path = os.path.join(patient_folder, matched_filename) # organ_image = sitk.ReadImage(organ_path) # if combined_image is None: # # 初始化合成影像 # combined_image = sitk.Image(organ_image.GetSize(), sitk.sitkUInt8) # combined_image.CopyInformation(organ_image) # # 重新取樣器官影像以匹配合成影像的大小 # resampled_organ_image = resample_image(organ_image, combined_image) # # 將器官影像添加到合成影像中 # organ_array = sitk.GetArrayFromImage(resampled_organ_image) # combined_array = sitk.GetArrayFromImage(combined_image) # combined_array[organ_array > 0] = int(label) # combined_image = sitk.GetImageFromArray(combined_array) # combined_image.CopyInformation(resampled_organ_image) # # 保存合成影像 # if combined_image: # output_filename = f'{image_type}_label_{filename}.nii.gz' # output_path = os.path.join(output_folder, output_filename) # sitk.WriteImage(combined_image, output_path) # print(f'Saved combined image to {output_path}') # file_counter += 1 # # 處理 T1C 影像 # print("Processing T1C images...") # process_images(T1C_folder_path, T1C_output_folder_path, 'T1C') # # 處理 CT+CTC 影像 # print("Processing CT+CTC images...") # process_images(CTCTC_folder_path, CTCTC_output_folder_path, 'CT+CTC') # print("All processing completed.") import os import SimpleITK as sitk import difflib # 定义文件夹路径 T1_folder_path = '/mnt/1218/onlylian/sorted_OAR_files_test/T1C' T1_output_folder_path = '/mnt/1218/onlylian/T1C_label_folder_test_2' # 定义组织标签对应关系 labels = { "background": "0", "Brainstem": "1", "Right_Eye": "2", "Left_Eye": "3", "Optic_Chiasm": "4", "Right_Optic_Nerve": "5", "Left_Optic_Nerve": "6" } # 确保输出文件夹存在 os.makedirs(T1_output_folder_path, exist_ok=True) def resample_image(image, reference_image): resample = sitk.ResampleImageFilter() resample.SetReferenceImage(reference_image) resample.SetInterpolator(sitk.sitkNearestNeighbor) resample.SetDefaultPixelValue(0) return resample.Execute(image) def process_images(input_folder, output_folder, image_type): # 获取所有病人的文件夹路径 patient_folders = sorted([os.path.join(dp, d) for dp, dn, filenames in os.walk(input_folder) for d in dn]) file_counter = 1 for patient_folder in patient_folders: # 创建空的图像，用来合成所有器官 combined_image = None # 获取病人文件夹中的所有文件名，并转换为小写形式 patient_files = {f.lower(): f for f in os.listdir(patient_folder)} filename = patient_folder.split('/')[-2] # 遍历每个病人的所有图像文件 for organ_name, label in labels.items(): organ_filename = f'struct_{organ_name}.nii.gz'.lower() # 使用 difflib.get_close_matches 查找相似文件名 possible_matches = difflib.get_close_matches(organ_filename, patient_files.keys(), n=1, cutoff=0.8) if possible_matches: matched_filename = patient_files[possible_matches[0]] organ_path = os.path.join(patient_folder, matched_filename) organ_image = sitk.ReadImage(organ_path) if combined_image is None: # 初始化合成图像 combined_image = sitk.Image(organ_image.GetSize(), sitk.sitkUInt8) combined_image.CopyInformation(organ_image) # 重新采样器官图像以匹配合成图像的大小 resampled_organ_image = resample_image(organ_image, combined_image) # 将器官图像添加到合成图像中 organ_array = sitk.GetArrayFromImage(resampled_organ_image) combined_array = sitk.GetArrayFromImage(combined_image) combined_array[organ_array > 0] = int(label) combined_image = sitk.GetImageFromArray(combined_array) combined_image.CopyInformation(resampled_organ_image) # 保存合成图像 if combined_image: output_filename = f'{image_type}_label_{filename}.nii.gz' output_path = os.path.join(output_folder, output_filename) sitk.WriteImage(combined_image, output_path) print(f'Saved combined image to {output_path}') file_counter += 1 # 处理 T1C 图像 print("Processing T1C images...") process_images(T1_folder_path, T1_output_folder_path, 'T1C') print("Processing completed.")