107 lines
4.6 KiB
Python
107 lines
4.6 KiB
Python
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import os
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import glob
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import SimpleITK as sitk
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from difflib import get_close_matches
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def process_images(input_base_path, output_base_path, output_prefix):
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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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"Struct_Brain_Stem": "1",
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"Struct_Right_Eye": "2",
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"Struct_Left_Eye": "3",
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"Struct_Optic_Chiasm": "4",
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"Struct_Right_Optic_Nerve": "5",
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"Struct_Left_Optic_Nerve": "6",
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"TV": "7" # Tumor
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}
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# 確保輸出資料夾存在
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os.makedirs(output_base_path, exist_ok=True)
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# 定義重新取樣函數
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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) # 使用最近鄰插值避免標籤變化
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resample.SetDefaultPixelValue(0)
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resampled_image = resample.Execute(image)
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return resampled_image
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# 遍歷所有病人的資料夾
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for patient_id in os.listdir(input_base_path):
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patient_folder_path = os.path.join(input_base_path, patient_id)
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if not os.path.isdir(patient_folder_path):
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continue # 跳過非資料夾的項目
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# 確定病人資料夾下的日期資料夾
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date_folder = os.listdir(patient_folder_path)[0]
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ORGAN_folder_path = os.path.join(patient_folder_path, date_folder)
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# 創建一個空的影像來合併所有標籤
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combined_image = None
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# 列出病人資料夾內的所有檔案
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all_files = os.listdir(ORGAN_folder_path)
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# 遍歷每個標籤並合併
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for organ_name, label in labels.items():
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if organ_name == "TV":
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# 動態搜尋 TV 檔案
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tv_files = glob.glob(os.path.join(ORGAN_folder_path, "TV-*.nii.gz"))
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if len(tv_files) == 0:
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print(f"TV file not found for patient {patient_id}, skipping TV...")
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continue
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organ_path = tv_files[0] # 取第一個找到的檔案
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else:
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# 使用模糊比對尋找最接近的檔案名
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matches = get_close_matches(organ_name, all_files, n=1, cutoff=0.6)
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if len(matches) == 0:
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print(f"No close match found for {organ_name} for patient {patient_id}, skipping...")
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continue
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organ_filename = matches[0]
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organ_path = os.path.join(ORGAN_folder_path, organ_filename)
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if not os.path.exists(organ_path):
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print(f"File {organ_path} not found for patient {patient_id}, skipping...")
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continue
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organ_image = sitk.ReadImage(organ_path)
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if combined_image is None:
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# 初始化合併影像的大小和參考
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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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else:
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# 如果器官影像的尺寸和合併影像不同，進行重新取樣
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if organ_image.GetSize() != combined_image.GetSize():
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print(f"Resampling organ {organ_name} for patient {patient_id} due to size mismatch...")
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organ_image = resample_image(organ_image, combined_image)
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# 將器官影像添加到合併影像中
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organ_array = sitk.GetArrayFromImage(organ_image)
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combined_array = sitk.GetArrayFromImage(combined_image)
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# 更新合併影像的陣列，根據標籤值來設定非背景區域
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combined_array[organ_array > 0] = int(label)
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# 將更新的陣列轉回 SimpleITK 影像
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combined_image = sitk.GetImageFromArray(combined_array)
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combined_image.CopyInformation(organ_image)
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# 保存合併後的影像
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output_filename = f'{output_prefix}_label_{patient_id}.nii.gz'
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output_path = os.path.join(output_base_path, output_filename)
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sitk.WriteImage(combined_image, output_path)
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print(f'Saved combined image for patient {patient_id} to {output_path}')
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if __name__ == "__main__":
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# 處理 T1C 影像
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T1C_input_path = '/home/onlylian/sorted_OAR_Target_files/T1C'
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T1C_output_path = '/home/onlylian/T1C_OAR_Target_label_folder'
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process_images(T1C_input_path, T1C_output_path, "T1C")
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# 處理 CT+CTC 影像
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CT_CTC_input_path = '/home/onlylian/sorted_OAR_Target_files/T1C' # 注意：這裡使用相同的輸入路徑，如果需要不同的路徑請修改
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CT_CTC_output_path = '/home/onlylian/CT+CTC_OAR_Target_label_folder'
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process_images(CT_CTC_input_path, CT_CTC_output_path, "CT+CTC")
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