開春版本

onlylian/oar4.py

@@ -125,6 +125,9 @@ LABEL_MAPPING: Dict[int, str] = {
     5: "Right_Optic_Nerve",
     6: "Left_Optic_Nerve"
 }
+    
+# MAX_OAR = max(LABEL_MAPPING.keys())
+max_existing_label = max(LABEL_MAPPING.keys())
 
 # 設定日誌
 logging.basicConfig(
@@ -346,7 +349,7 @@ def process_multiple_tumors(image_array):
         return result_img
         
     # 為每個腫瘤生成唯一的標籤值，從最大現有標籤值開始
-    max_existing_label = max(LABEL_MAPPING.keys())
+#     max_existing_label = max(LABEL_MAPPING.keys())
     tumor_colors = generate_distinct_colors(num_tumors)
     
     # 更新標籤映射和顏色映射
@@ -374,37 +377,49 @@ def create_structure_masks(image: sitk.Image) -> Dict[str, np.ndarray]:
             
     return masks
 
-def register(ct_fixed, moving, out_moving):
+MODELS = (
+    'rigid', 
+    'affine', 
+    'joint',
+)
 
-    with tempfile.TemporaryDirectory() as tmp:
+def register(ct_fixed, moving):
-        fixed = os.path.join(tmp, 'clipped.nii.gz')    
+
-    
+    out_dir = tempfile.mkdtemp(dir=Path(__file__).resolve().parent/'0')
-        ct = sf.load_volume(ct_fixed)
-        clipped = ct.clip(0, 80)
-        clipped.save(fixed)
         
-        FREESURFER_HOME = str(Path(__file__).resolve().parent/'mri_synthmorph')
+    fixed = os.path.join(out_dir, 'clipped.nii.gz')    
 
+    ct = sf.load_volume(ct_fixed)
+    clipped = ct.clip(0, 80)
+    clipped.save(fixed)
+
+    FREESURFER_HOME = Path(__file__).resolve().parent/'mri_synthmorph'
+    my_env = os.environ.copy()
+    my_env["FREESURFER_HOME"] = str(FREESURFER_HOME)
+
+    mri_synthmorph = str(FREESURFER_HOME/'mri_synthmorph')
+    
+    for m in MODELS:
+        
+        transform_extension = 'lta' if m in ('affine','rigid') else 'nii.gz'
+    
         args = [
-            str(Path(__file__).resolve().parent/'mri_synthmorph/mri_synthmorph'),
+            mri_synthmorph,
-#             'register',
+            '-m', m,
-            '-m', 'affine',
+            '-o', os.path.join(out_dir, '%s.nii.gz'%m),
-#             '-m', 'rigid',
+            
-            '-o', out_moving,
+            '-O', os.path.join(out_dir, 'out_fixed-%s.nii.gz'%m),
+            '-t', os.path.join(out_dir, 'moving_to_fixed-%s.%s'% (m, transform_extension)),
+            '-T', os.path.join(out_dir, 'fixed_to_moving-%s.%s'% (m, transform_extension)),
+            
             '-g',
             moving,
             fixed,
         ]
+        logger.info(f" 執行registration 指令：{' '.join(args)}")
+        result = subprocess.run(args, env=my_env)
 
-        my_env = os.environ.copy()
+    return out_dir
-        my_env["FREESURFER_HOME"] = str(Path(__file__).resolve().parent/'mri_synthmorph')
-        
-        
-#         logger.info(f"執行推論指令：{' '.join(predict_cmd)}")
-        result = subprocess.run(
-            args, 
-            env=my_env,
-        )
 
 '''
 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)
@@ -455,8 +470,8 @@ def register_synthmorph(ct_fixed, moving, out_moving):
 
         arg=argparse.Namespace(**default)
         FREESURFER_HOME = str(Path(__file__).resolve().parent/'mri_synthmorph')
-        print(arg)
+#         print(arg)
-        print(FREESURFER_HOME)
+#         print(FREESURFER_HOME)
 
         os.environ["FREESURFER_HOME"] = str(Path(__file__).resolve().parent/'mri_synthmorph')
         registration.register(arg)
@@ -476,11 +491,7 @@ def inference(DCM_CT, DCM_MR):
         ROOT_DIR = os.path.dirname(os.path.dirname(DCM_CT))
         NII_DIR = os.path.join(ROOT_DIR, 'nii')
         INPUT_DIR = os.path.join(ROOT_DIR, 'input')
-        OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
+        OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')      
-        
-        # 設定 RTSTRUCT 輸出路徑
-        head, tail = os.path.split(DCM_CT)
-        rtss_file = os.path.join(head, tail+'-rtss.dcm')
         
         # 清理並建立工作目錄
         for dir_path in [NII_DIR, INPUT_DIR, OUTPUT_DIR]:
@@ -519,26 +530,26 @@ def inference(DCM_CT, DCM_MR):
             raise FileNotFoundError(f"找不到 CT 的 NIfTI 檔案，目錄內容：{os.listdir(NII_DIR)}")
         if not NII_MR:
             raise FileNotFoundError(f"找不到 MR 的 NIfTI 檔案，目錄內容：{os.listdir(NII_DIR)}")
-        
+
+            
+        logger.info("Registration of %s and %s..."%(NII_CT, NII_MR))    
+        out_dir = register(NII_CT, NII_MR)
+            
         # 準備輸入檔案
         
         basename = os.path.basename(NII_MR)
         
         old = '_'+basename.split('_')[-1]
         
-        input_ct = os.path.join(INPUT_DIR, basename.replace(old, '_0000.nii.gz'))
+        for m in MODELS:
-        shutil.copy(NII_CT, input_ct)
+            input_ct = os.path.join(INPUT_DIR, basename.replace(old, '_%s_0000.nii.gz'%m))
+            shutil.copy(NII_CT, input_ct)
 
-        input_mr = os.path.join(INPUT_DIR, basename.replace(old, '_0001.nii.gz'))
+            input_mr = os.path.join(INPUT_DIR, basename.replace(old, '_%s_0001.nii.gz'%m))
-        
+            shutil.copy(os.path.join(out_dir, '%s.nii.gz'%m), input_mr)
-        logger.info("Registration of %s and %s..."%(NII_CT, NII_MR))    
+            
-        register(NII_CT, NII_MR, input_mr)              
+#         shutil.rmtree(out_dir)
 
-        output_file = os.path.join(OUTPUT_DIR, basename.replace(old, '.nii.gz'))       
-
-        basename_ct = os.path.basename(NII_CT)
-        old_ct = '_'+basename_ct.split('_')[-1]
-        label_file = os.path.join(NII_DIR, basename_ct.replace(old_ct, '.label.nii.gz'))
         
 #         basename = os.path.basename(NII_MR)
 #         old = '_'+basename.split('_')[-1]
@@ -597,129 +608,133 @@ def inference(DCM_CT, DCM_MR):
         except subprocess.CalledProcessError as e:
             logger.error(f"模型推論失敗：\n輸出：{e.output}\n錯誤：{e.stderr}")
             raise RuntimeError(f"模型推論失敗：{e.stderr}")
-        
-        if not os.path.exists(output_file):
-            raise FileNotFoundError(f"找不到模型輸出檔案：{output_file}")
-        
-        logger.info("開始執行影像後處理...")
-        
-        try:
-            # 讀取預測結果
-            predicted_image = sitk.ReadImage(output_file)
-            predicted_image = sitk.DICOMOrient(predicted_image)
-            predicted_array = sitk.GetArrayFromImage(predicted_image)
             
-            # 執行後處理
+        rtss_combination = []
-            processed_array = process_symmetric_structures(predicted_array)
-            processed_array = process_multiple_tumors(processed_array)
-            
-            processed_image = sitk.GetImageFromArray(processed_array)
-            processed_image.CopyInformation(predicted_image)
-            
-            # 暫時保存後處理結果
-            processed_output = os.path.join(OUTPUT_DIR, 'processed_' + os.path.basename(output_file))
-            sitk.WriteImage(processed_image, processed_output)
-            logger.info(f"後處理結果已保存至：{processed_output}")
-            
-            logger.info("開始執行影像配準...")
-#             reg_transform(NII_CT, NII_MR, processed_output, label_file)
-            shutil.copy(processed_output, label_file)
-            
-        except Exception as e:
-            logger.error(f"影像處理失敗：{str(e)}")
-            raise RuntimeError("後處理或配準失敗")
-        
-        if not os.path.exists(label_file):
-            raise FileNotFoundError(f"找不到配準後的標籤檔案：{label_file}")
-        
-        logger.info("開始建立 RTSTRUCT...")
-        
-        # 讀取 CT 影像序列
-        try:
-            reader = sitk.ImageSeriesReader()
-            dicom_names = reader.GetGDCMSeriesFileNames(DCM_CT)
-            reader.SetFileNames(dicom_names)
-            reader.MetaDataDictionaryArrayUpdateOn()
-            reader.LoadPrivateTagsOn()
-            image = reader.Execute()
-        except Exception as e:
-            logger.error(f"讀取 CT 系列失敗：{str(e)}")
-            raise RuntimeError("無法讀取 CT 影像系列")
-        
-        # 讀取並重新取樣配準後的預測結果
-        try:
-            final_image = sitk.ReadImage(label_file)
-            final_image = sitk.Resample(
-                final_image, 
-                image, 
-                sitk.Transform(), 
-                sitk.sitkNearestNeighbor
-            )
-        except Exception as e:
-            logger.error(f"重新取樣失敗：{str(e)}")
-            raise RuntimeError("預測結果重新取樣失敗")
-        
-        # 建立 RTSTRUCT
-        try:
 
-            images = []
+        for m in MODELS:
-            images.append(IntensityImage(image, Modality('CT')))
+            
-#             images.append(IntensityImage(sitk.ReadImage(input_mr), Modality('MR')))
+            basename_ct = os.path.basename(NII_CT)
+            old_ct = '_'+basename_ct.split('_')[-1]
+            label_file = os.path.join(NII_DIR, basename_ct.replace(old_ct, '_%s.label.nii.gz'%m))
+
+            output_file = os.path.join(OUTPUT_DIR, basename.replace(old, '_%s.nii.gz'%m))
+            
+            if not os.path.exists(output_file):
+                raise FileNotFoundError(f"找不到模型輸出檔案：{output_file}")
+
+            logger.info("開始執行影像後處理...")
+
+            try:
+                # 讀取預測結果
+                predicted_image = sitk.ReadImage(output_file)
+                predicted_image = sitk.DICOMOrient(predicted_image)
+                predicted_array = sitk.GetArrayFromImage(predicted_image)
+
+                # 執行後處理
+                processed_array = process_symmetric_structures(predicted_array)
+                processed_array = process_multiple_tumors(processed_array)
+
+                processed_image = sitk.GetImageFromArray(processed_array)
+                processed_image.CopyInformation(predicted_image)
+
+                # 暫時保存後處理結果
+                processed_output = os.path.join(OUTPUT_DIR, 'processed_' + os.path.basename(output_file))
+                sitk.WriteImage(processed_image, processed_output)
+                logger.info(f"後處理結果已保存至：{processed_output}")
+
+                logger.info("開始執行影像配準...")
+    #             reg_transform(NII_CT, NII_MR, processed_output, label_file)
+                shutil.copy(processed_output, label_file)
+
+            except Exception as e:
+                logger.error(f"影像處理失敗：{str(e)}")
+                raise RuntimeError("後處理或配準失敗")
+
+            if not os.path.exists(label_file):
+                raise FileNotFoundError(f"找不到配準後的標籤檔案：{label_file}")
+
+            logger.info("開始建立 RTSTRUCT...")
+
+            # 讀取 CT 影像序列
+            try:
+                reader = sitk.ImageSeriesReader()
+                dicom_names = reader.GetGDCMSeriesFileNames(DCM_CT)
+                reader.SetFileNames(dicom_names)
+                reader.MetaDataDictionaryArrayUpdateOn()
+                reader.LoadPrivateTagsOn()
+                image = reader.Execute()
+            except Exception as e:
+                logger.error(f"讀取 CT 系列失敗：{str(e)}")
+                raise RuntimeError("無法讀取 CT 影像系列")
+
+            # 讀取並重新取樣配準後的預測結果
+            try:
+                final_image = sitk.ReadImage(label_file)
+                final_image = sitk.Resample(
+                    final_image, 
+                    image, 
+                    sitk.Transform(), 
+                    sitk.sitkNearestNeighbor
+                )
+            except Exception as e:
+                logger.error(f"重新取樣失敗：{str(e)}")
+                raise RuntimeError("預測結果重新取樣失敗")
+
+            # 建立 RTSTRUCT
+            try:
+
+                images = []
+                images.append(IntensityImage(image, Modality('CT')))
+    #             images.append(IntensityImage(sitk.ReadImage(input_mr), Modality('MR')))
+
+                for k, v in LABEL_MAPPING.items():
+                    images.append(SegmentationImage(final_image==k, v))
+
+                dcm_crawler = ps_io.SubjectDicomCrawler(DCM_CT,
+                    modality_extractor=ExampleModalityExtractor(),
+                    )
+                dicom_series_info = dcm_crawler.execute()
+
+                subject = Subject(dicom_series_info[0].get_patient_id(), images)                  
+
+                reference_modality = 'CT'
+
+                use_3d_conversion = True
+                if use_3d_conversion:
+                    conv_conf = ps_io.RTSSConverter3DConfiguration()
+                else:
+                    conv_conf = ps_io.RTSSConverter2DConfiguration()
+                meta_data = ps_io.RTSSMetaData(
+                    series_description = '%s by %s'%(m, type(conv_conf).__name__)
+                )
+
+                converter = ps_io.SubjectToRTSSConverter(
+                    subject,
+                    dicom_series_info,
+                    reference_modality,
+                    conv_conf,
+                    meta_data,
+    #                 colors = tuple(LABEL_COLORS.values()),
+                    )
+
+                rtss = converter.convert()
+
+                # 設定 RTSTRUCT 輸出路徑
+                head, tail = os.path.split(DCM_CT)
+                rtss_file = os.path.join(head, tail+'_%s-rtss.dcm'%m)
+
+                # Write the DICOM-RTSS to a separate subject directory
+                # and include the DICOM files crawled before
+                # Note: If you want to output just a subset of the
+                # original DICOM files you may use additional selectors
+
+                output_dir_path =  os.path.join(ROOT_DIR, 'pyradise')
+                rtss_filename = os.path.basename(rtss_file)
                 
-            for k, v in LABEL_MAPPING.items():
+                rtss_combination.append((rtss_filename, rtss))
-                images.append(SegmentationImage(final_image==k, v))
 
-            dcm_crawler = ps_io.SubjectDicomCrawler(DCM_CT,
+                
-                modality_extractor=ExampleModalityExtractor(),
+#             rtss_combination = ((rtss_filename, rtss),)
-                )
-            dicom_series_info = dcm_crawler.execute()
-
-            subject = Subject(dicom_series_info[0].get_patient_id(), images)                  
-            
-            reference_modality = 'CT'
-
-            use_3d_conversion = True
-            if use_3d_conversion:
-                conv_conf = ps_io.RTSSConverter3DConfiguration()
-            else:
-                conv_conf = ps_io.RTSSConverter2DConfiguration()
-            meta_data = ps_io.RTSSMetaData(
-                series_description = '%s by onlylian'%type(conv_conf).__name__)
-
-            converter = ps_io.SubjectToRTSSConverter(
-                subject,
-                dicom_series_info,
-                reference_modality,
-                conv_conf,
-                meta_data,
-#                 colors = tuple(LABEL_COLORS.values()),
-                )
-            
-            rtss = converter.convert()
-
-
-            # Write the DICOM-RTSS to a separate subject directory
-            # and include the DICOM files crawled before
-            # Note: If you want to output just a subset of the
-            # original DICOM files you may use additional selectors
-            
-            output_dir_path =  os.path.join(ROOT_DIR, 'pyradise')
-            rtss_filename = os.path.basename(rtss_file)
-            rtss_combination = ((rtss_filename, rtss),)
-            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,)
-            
-            shutil.copy(os.path.join(output_dir_path, rtss_filename), rtss_file)
             
 #             rtstruct = RTStructBuilder.create_new(dicom_series_path=DCM_CT)
             
@@ -748,16 +763,30 @@ def inference(DCM_CT, DCM_MR):
 #             logger.info(f"儲存 RTSTRUCT：{rtss_file}")
 #             rtstruct.save(rtss_file)
             
-        except Exception as e:
+            except Exception as e:
-            logger.error(f"RTSTRUCT 生成失敗：{str(e)}")
+                logger.error(f"RTSTRUCT 生成失敗：{str(e)}")
-            raise RuntimeError("無法生成或儲存 RTSTRUCT")
+                raise RuntimeError("無法生成或儲存 RTSTRUCT")
-        
+
-        return rtss_file
+#         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)