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