開春版本
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Furen
parent
7cfd15c6c3
commit
8802d67cda
1 changed files with 263 additions and 168 deletions
431
onlylian/oar4.py
431
onlylian/oar4.py
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@ -125,6 +125,9 @@ LABEL_MAPPING: Dict[int, str] = {
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5: "Right_Optic_Nerve",
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6: "Left_Optic_Nerve"
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}
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# MAX_OAR = max(LABEL_MAPPING.keys())
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max_existing_label = max(LABEL_MAPPING.keys())
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# 設定日誌
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logging.basicConfig(
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@ -346,7 +349,7 @@ def process_multiple_tumors(image_array):
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return result_img
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# 為每個腫瘤生成唯一的標籤值，從最大現有標籤值開始
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max_existing_label = max(LABEL_MAPPING.keys())
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# max_existing_label = max(LABEL_MAPPING.keys())
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tumor_colors = generate_distinct_colors(num_tumors)
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# 更新標籤映射和顏色映射
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@ -374,37 +377,49 @@ def create_structure_masks(image: sitk.Image) -> Dict[str, np.ndarray]:
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return masks
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def register(ct_fixed, moving, out_moving):
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MODELS = (
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'rigid',
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'affine',
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'joint',
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)
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with tempfile.TemporaryDirectory() as tmp:
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fixed = os.path.join(tmp, 'clipped.nii.gz')
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ct = sf.load_volume(ct_fixed)
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clipped = ct.clip(0, 80)
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clipped.save(fixed)
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def register(ct_fixed, moving):
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out_dir = tempfile.mkdtemp(dir=Path(__file__).resolve().parent/'0')
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FREESURFER_HOME = str(Path(__file__).resolve().parent/'mri_synthmorph')
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fixed = os.path.join(out_dir, 'clipped.nii.gz')
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ct = sf.load_volume(ct_fixed)
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clipped = ct.clip(0, 80)
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clipped.save(fixed)
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FREESURFER_HOME = Path(__file__).resolve().parent/'mri_synthmorph'
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my_env = os.environ.copy()
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my_env["FREESURFER_HOME"] = str(FREESURFER_HOME)
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mri_synthmorph = str(FREESURFER_HOME/'mri_synthmorph')
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for m in MODELS:
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transform_extension = 'lta' if m in ('affine','rigid') else 'nii.gz'
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args = [
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str(Path(__file__).resolve().parent/'mri_synthmorph/mri_synthmorph'),
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# 'register',
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'-m', 'affine',
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# '-m', 'rigid',
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'-o', out_moving,
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mri_synthmorph,
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'-m', m,
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'-o', os.path.join(out_dir, '%s.nii.gz'%m),
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'-O', os.path.join(out_dir, 'out_fixed-%s.nii.gz'%m),
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'-t', os.path.join(out_dir, 'moving_to_fixed-%s.%s'% (m, transform_extension)),
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'-T', os.path.join(out_dir, 'fixed_to_moving-%s.%s'% (m, transform_extension)),
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'-g',
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moving,
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fixed,
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]
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logger.info(f" 執行registration 指令：{' '.join(args)}")
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result = subprocess.run(args, env=my_env)
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my_env = os.environ.copy()
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my_env["FREESURFER_HOME"] = str(Path(__file__).resolve().parent/'mri_synthmorph')
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# logger.info(f"執行推論指令：{' '.join(predict_cmd)}")
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result = subprocess.run(
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args,
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env=my_env,
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)
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return out_dir
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'''
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Namespace(command='register', extent=256, fixed='0/clipped.nii.gz', gpu=True, header_only=False, hyper=0.5, init=None, inverse=None, mid_space=False, model='affine', moving='0/t1c.nii.gz', out_dir=None, out_fixed=None, out_moving='0/out-aff.nii.gz', steps=7, threads=None, trans='0/trans.lta', verbose=False, weights=None)
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@ -455,8 +470,8 @@ def register_synthmorph(ct_fixed, moving, out_moving):
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arg=argparse.Namespace(**default)
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FREESURFER_HOME = str(Path(__file__).resolve().parent/'mri_synthmorph')
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print(arg)
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print(FREESURFER_HOME)
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# print(arg)
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# print(FREESURFER_HOME)
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os.environ["FREESURFER_HOME"] = str(Path(__file__).resolve().parent/'mri_synthmorph')
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registration.register(arg)
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@ -476,11 +491,7 @@ def inference(DCM_CT, DCM_MR):
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ROOT_DIR = os.path.dirname(os.path.dirname(DCM_CT))
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NII_DIR = os.path.join(ROOT_DIR, 'nii')
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INPUT_DIR = os.path.join(ROOT_DIR, 'input')
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OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
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# 設定 RTSTRUCT 輸出路徑
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head, tail = os.path.split(DCM_CT)
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rtss_file = os.path.join(head, tail+'-rtss.dcm')
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OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
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# 清理並建立工作目錄
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for dir_path in [NII_DIR, INPUT_DIR, OUTPUT_DIR]:
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@ -519,26 +530,26 @@ def inference(DCM_CT, DCM_MR):
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raise FileNotFoundError(f"找不到 CT 的 NIfTI 檔案，目錄內容：{os.listdir(NII_DIR)}")
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if not NII_MR:
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raise FileNotFoundError(f"找不到 MR 的 NIfTI 檔案，目錄內容：{os.listdir(NII_DIR)}")
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logger.info("Registration of %s and %s..."%(NII_CT, NII_MR))
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out_dir = register(NII_CT, NII_MR)
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# 準備輸入檔案
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basename = os.path.basename(NII_MR)
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old = '_'+basename.split('_')[-1]
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input_ct = os.path.join(INPUT_DIR, basename.replace(old, '_0000.nii.gz'))
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shutil.copy(NII_CT, input_ct)
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for m in MODELS:
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input_ct = os.path.join(INPUT_DIR, basename.replace(old, '_%s_0000.nii.gz'%m))
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shutil.copy(NII_CT, input_ct)
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input_mr = os.path.join(INPUT_DIR, basename.replace(old, '_0001.nii.gz'))
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logger.info("Registration of %s and %s..."%(NII_CT, NII_MR))
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register(NII_CT, NII_MR, input_mr)
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input_mr = os.path.join(INPUT_DIR, basename.replace(old, '_%s_0001.nii.gz'%m))
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shutil.copy(os.path.join(out_dir, '%s.nii.gz'%m), input_mr)
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# shutil.rmtree(out_dir)
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output_file = os.path.join(OUTPUT_DIR, basename.replace(old, '.nii.gz'))
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basename_ct = os.path.basename(NII_CT)
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old_ct = '_'+basename_ct.split('_')[-1]
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label_file = os.path.join(NII_DIR, basename_ct.replace(old_ct, '.label.nii.gz'))
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# basename = os.path.basename(NII_MR)
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# old = '_'+basename.split('_')[-1]
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@ -597,129 +608,133 @@ def inference(DCM_CT, DCM_MR):
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except subprocess.CalledProcessError as e:
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logger.error(f"模型推論失敗：\n輸出：{e.output}\n錯誤：{e.stderr}")
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raise RuntimeError(f"模型推論失敗：{e.stderr}")
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if not os.path.exists(output_file):
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raise FileNotFoundError(f"找不到模型輸出檔案：{output_file}")
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logger.info("開始執行影像後處理...")
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try:
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# 讀取預測結果
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predicted_image = sitk.ReadImage(output_file)
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predicted_image = sitk.DICOMOrient(predicted_image)
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predicted_array = sitk.GetArrayFromImage(predicted_image)
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# 執行後處理
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processed_array = process_symmetric_structures(predicted_array)
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processed_array = process_multiple_tumors(processed_array)
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processed_image = sitk.GetImageFromArray(processed_array)
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processed_image.CopyInformation(predicted_image)
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# 暫時保存後處理結果
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processed_output = os.path.join(OUTPUT_DIR, 'processed_' + os.path.basename(output_file))
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sitk.WriteImage(processed_image, processed_output)
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logger.info(f"後處理結果已保存至：{processed_output}")
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logger.info("開始執行影像配準...")
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# reg_transform(NII_CT, NII_MR, processed_output, label_file)
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shutil.copy(processed_output, label_file)
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except Exception as e:
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logger.error(f"影像處理失敗：{str(e)}")
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raise RuntimeError("後處理或配準失敗")
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if not os.path.exists(label_file):
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raise FileNotFoundError(f"找不到配準後的標籤檔案：{label_file}")
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logger.info("開始建立 RTSTRUCT...")
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# 讀取 CT 影像序列
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try:
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reader = sitk.ImageSeriesReader()
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dicom_names = reader.GetGDCMSeriesFileNames(DCM_CT)
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reader.SetFileNames(dicom_names)
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reader.MetaDataDictionaryArrayUpdateOn()
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reader.LoadPrivateTagsOn()
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image = reader.Execute()
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except Exception as e:
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logger.error(f"讀取 CT 系列失敗：{str(e)}")
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raise RuntimeError("無法讀取 CT 影像系列")
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# 讀取並重新取樣配準後的預測結果
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try:
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final_image = sitk.ReadImage(label_file)
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final_image = sitk.Resample(
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final_image,
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image,
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sitk.Transform(),
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sitk.sitkNearestNeighbor
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)
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except Exception as e:
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logger.error(f"重新取樣失敗：{str(e)}")
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raise RuntimeError("預測結果重新取樣失敗")
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# 建立 RTSTRUCT
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try:
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rtss_combination = []
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images = []
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images.append(IntensityImage(image, Modality('CT')))
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# images.append(IntensityImage(sitk.ReadImage(input_mr), Modality('MR')))
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for m in MODELS:
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basename_ct = os.path.basename(NII_CT)
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old_ct = '_'+basename_ct.split('_')[-1]
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label_file = os.path.join(NII_DIR, basename_ct.replace(old_ct, '_%s.label.nii.gz'%m))
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output_file = os.path.join(OUTPUT_DIR, basename.replace(old, '_%s.nii.gz'%m))
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if not os.path.exists(output_file):
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raise FileNotFoundError(f"找不到模型輸出檔案：{output_file}")
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logger.info("開始執行影像後處理...")
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try:
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# 讀取預測結果
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predicted_image = sitk.ReadImage(output_file)
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predicted_image = sitk.DICOMOrient(predicted_image)
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predicted_array = sitk.GetArrayFromImage(predicted_image)
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# 執行後處理
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processed_array = process_symmetric_structures(predicted_array)
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processed_array = process_multiple_tumors(processed_array)
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processed_image = sitk.GetImageFromArray(processed_array)
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processed_image.CopyInformation(predicted_image)
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# 暫時保存後處理結果
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processed_output = os.path.join(OUTPUT_DIR, 'processed_' + os.path.basename(output_file))
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sitk.WriteImage(processed_image, processed_output)
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logger.info(f"後處理結果已保存至：{processed_output}")
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logger.info("開始執行影像配準...")
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# reg_transform(NII_CT, NII_MR, processed_output, label_file)
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shutil.copy(processed_output, label_file)
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except Exception as e:
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logger.error(f"影像處理失敗：{str(e)}")
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raise RuntimeError("後處理或配準失敗")
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if not os.path.exists(label_file):
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raise FileNotFoundError(f"找不到配準後的標籤檔案：{label_file}")
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logger.info("開始建立 RTSTRUCT...")
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# 讀取 CT 影像序列
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try:
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reader = sitk.ImageSeriesReader()
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dicom_names = reader.GetGDCMSeriesFileNames(DCM_CT)
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reader.SetFileNames(dicom_names)
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reader.MetaDataDictionaryArrayUpdateOn()
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reader.LoadPrivateTagsOn()
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image = reader.Execute()
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except Exception as e:
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logger.error(f"讀取 CT 系列失敗：{str(e)}")
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raise RuntimeError("無法讀取 CT 影像系列")
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# 讀取並重新取樣配準後的預測結果
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try:
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final_image = sitk.ReadImage(label_file)
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final_image = sitk.Resample(
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final_image,
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image,
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sitk.Transform(),
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sitk.sitkNearestNeighbor
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)
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except Exception as e:
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logger.error(f"重新取樣失敗：{str(e)}")
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raise RuntimeError("預測結果重新取樣失敗")
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# 建立 RTSTRUCT
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try:
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images = []
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images.append(IntensityImage(image, Modality('CT')))
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# images.append(IntensityImage(sitk.ReadImage(input_mr), Modality('MR')))
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for k, v in LABEL_MAPPING.items():
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images.append(SegmentationImage(final_image==k, v))
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dcm_crawler = ps_io.SubjectDicomCrawler(DCM_CT,
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modality_extractor=ExampleModalityExtractor(),
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)
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dicom_series_info = dcm_crawler.execute()
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subject = Subject(dicom_series_info[0].get_patient_id(), images)
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reference_modality = 'CT'
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use_3d_conversion = True
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if use_3d_conversion:
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conv_conf = ps_io.RTSSConverter3DConfiguration()
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else:
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conv_conf = ps_io.RTSSConverter2DConfiguration()
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meta_data = ps_io.RTSSMetaData(
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series_description = '%s by %s'%(m, type(conv_conf).__name__)
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)
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converter = ps_io.SubjectToRTSSConverter(
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subject,
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dicom_series_info,
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reference_modality,
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conv_conf,
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meta_data,
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# colors = tuple(LABEL_COLORS.values()),
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)
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rtss = converter.convert()
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# 設定 RTSTRUCT 輸出路徑
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head, tail = os.path.split(DCM_CT)
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rtss_file = os.path.join(head, tail+'_%s-rtss.dcm'%m)
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# Write the DICOM-RTSS to a separate subject directory
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# and include the DICOM files crawled before
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# Note: If you want to output just a subset of the
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# original DICOM files you may use additional selectors
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output_dir_path = os.path.join(ROOT_DIR, 'pyradise')
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rtss_filename = os.path.basename(rtss_file)
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for k, v in LABEL_MAPPING.items():
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images.append(SegmentationImage(final_image==k, v))
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rtss_combination.append((rtss_filename, rtss))
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dcm_crawler = ps_io.SubjectDicomCrawler(DCM_CT,
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modality_extractor=ExampleModalityExtractor(),
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)
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dicom_series_info = dcm_crawler.execute()
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subject = Subject(dicom_series_info[0].get_patient_id(), images)
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reference_modality = 'CT'
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use_3d_conversion = True
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if use_3d_conversion:
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conv_conf = ps_io.RTSSConverter3DConfiguration()
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else:
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conv_conf = ps_io.RTSSConverter2DConfiguration()
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meta_data = ps_io.RTSSMetaData(
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series_description = '%s by onlylian'%type(conv_conf).__name__)
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converter = ps_io.SubjectToRTSSConverter(
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subject,
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dicom_series_info,
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reference_modality,
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conv_conf,
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meta_data,
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# colors = tuple(LABEL_COLORS.values()),
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)
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rtss = converter.convert()
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# Write the DICOM-RTSS to a separate subject directory
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# and include the DICOM files crawled before
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# Note: If you want to output just a subset of the
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# original DICOM files you may use additional selectors
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output_dir_path = os.path.join(ROOT_DIR, 'pyradise')
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rtss_filename = os.path.basename(rtss_file)
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rtss_combination = ((rtss_filename, rtss),)
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print(output_dir_path)
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print(rtss_combination)
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# shutil.rmtree(output_dir_path, ignore_errors=True)
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os.makedirs(output_dir_path, exist_ok=True)
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writer = ps_io.DicomSeriesSubjectWriter()
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writer.write(rtss_combination,
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output_dir_path,
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# subject.get_name(),
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None,
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dicom_series_info,)
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shutil.copy(os.path.join(output_dir_path, rtss_filename), rtss_file)
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# rtss_combination = ((rtss_filename, rtss),)
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# rtstruct = RTStructBuilder.create_new(dicom_series_path=DCM_CT)
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@ -748,16 +763,30 @@ def inference(DCM_CT, DCM_MR):
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# logger.info(f"儲存 RTSTRUCT：{rtss_file}")
|
||||
# rtstruct.save(rtss_file)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"RTSTRUCT 生成失敗：{str(e)}")
|
||||
raise RuntimeError("無法生成或儲存 RTSTRUCT")
|
||||
|
||||
return rtss_file
|
||||
except Exception as e:
|
||||
logger.error(f"RTSTRUCT 生成失敗：{str(e)}")
|
||||
raise RuntimeError("無法生成或儲存 RTSTRUCT")
|
||||
|
||||
# print(output_dir_path)
|
||||
# print(rtss_combination)
|
||||
|
||||
# shutil.rmtree(output_dir_path, ignore_errors=True)
|
||||
os.makedirs(output_dir_path, exist_ok=True)
|
||||
|
||||
writer = ps_io.DicomSeriesSubjectWriter()
|
||||
writer.write(rtss_combination,
|
||||
output_dir_path,
|
||||
# subject.get_name(),
|
||||
None,
|
||||
dicom_series_info,)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"推論過程發生錯誤：{str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
return rtss_combination, output_dir_path
|
||||
|
||||
|
||||
def SendDCM(fp):
|
||||
"""傳送 DICOM 檔案到 PACS"""
|
||||
logger.info(f"準備傳送 DICOM 檔案：{fp}")
|
||||
|
|
@ -803,6 +832,62 @@ def SendDCM(fp):
|
|||
logger.error(f"DICOM 傳送過程發生錯誤：{str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
def SendDCMs(filelist):
|
||||
"""傳送 DICOM 檔案到 PACS"""
|
||||
logger.info(f"準備傳送 DICOM 檔案：{' '.join(filelist)}")
|
||||
debug_logger()
|
||||
|
||||
dslist = []
|
||||
|
||||
for fp in filelist:
|
||||
|
||||
if not os.path.exists(fp):
|
||||
raise FileNotFoundError(f"找不到 DICOM 檔案：{fp}")
|
||||
|
||||
try:
|
||||
ds = dcmread(fp)
|
||||
dslist.append(ds)
|
||||
except Exception as e:
|
||||
logger.error(f"讀取 DICOM 檔案失敗：{str(e)}")
|
||||
raise RuntimeError("無法讀取 DICOM 檔案")
|
||||
|
||||
|
||||
|
||||
try:
|
||||
ae = AE()
|
||||
ae.ae_title = 'OUR_STORE_SCP'
|
||||
ae.add_requested_context(RTStructureSetStorage)
|
||||
|
||||
|
||||
logger.info("正在連接 PACS 伺服器...")
|
||||
try:
|
||||
assoc = ae.associate("172.16.40.36", 104, ae_title='N1000_STORAGE')
|
||||
except Exception as e:
|
||||
logger.error(f"PACS 連接失敗：{str(e)}")
|
||||
raise RuntimeError("無法連接 PACS 伺服器")
|
||||
|
||||
if assoc.is_established:
|
||||
try:
|
||||
for ds in dslist:
|
||||
status = assoc.send_c_store(ds)
|
||||
if status:
|
||||
logger.info(f'DICOM 傳送成功，狀態碼: 0x{status.Status:04x}')
|
||||
else:
|
||||
raise RuntimeError('傳送失敗：連接逾時或收到無效回應')
|
||||
except Exception as e:
|
||||
logger.error(f"DICOM 傳送失敗：{str(e)}")
|
||||
raise
|
||||
finally:
|
||||
assoc.release()
|
||||
else:
|
||||
raise RuntimeError('傳送失敗：無法建立連接')
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"DICOM 傳送過程發生錯誤：{str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
def main():
|
||||
"""主程式進入點"""
|
||||
if len(sys.argv) < 3:
|
||||
|
|
@ -817,8 +902,18 @@ def main():
|
|||
|
||||
start_time = time.time()
|
||||
try:
|
||||
rtss_file = inference(sys.argv[1], sys.argv[2])
|
||||
SendDCM(rtss_file)
|
||||
|
||||
rtss_combination, output_dir_path = inference(sys.argv[1], sys.argv[2])
|
||||
|
||||
RTSS = []
|
||||
for rtss_filename, rtss in rtss_combination:
|
||||
rtss_file = os.path.join(output_dir_path, rtss_filename)
|
||||
shutil.copy(rtss_file, Path(sys.argv[1]).resolve().parent)
|
||||
RTSS.append(rtss_file)
|
||||
# SendDCM(rtss_file)
|
||||
|
||||
SendDCMs(RTSS)
|
||||
|
||||
logger.info(f"處理完成，總耗時：{time.time() - start_time:.2f} 秒")
|
||||
except Exception as e:
|
||||
logger.error(f"處理過程發生錯誤：{str(e)}", exc_info=True)
|
||||
|
|
|
|||
Loading…
Reference in a new issue