diff --git a/examples/apps/ai_livertumor_seg_app/app.py b/examples/apps/ai_livertumor_seg_app/app.py
index acca14d2..662c5fbb 100644
--- a/examples/apps/ai_livertumor_seg_app/app.py
+++ b/examples/apps/ai_livertumor_seg_app/app.py
@@ -23,6 +23,27 @@
 from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
 from monai.deploy.operators.publisher_operator import PublisherOperator
 
+# This is a sample series selection rule in JSON, simply selecting CT series.
+# If the study has more than 1 CT series, then all of them will be selected.
+# Please see more detail in DICOMSeriesSelectorOperator.
+# For list of string values, e.g. "ImageType": ["PRIMARY", "ORIGINAL"], it is a match if all elements
+# are all in the multi-value attribute of the DICOM series.
+
+Sample_Rules_Text = """
+{
+    "selections": [
+        {
+            "name": "CT Series",
+            "conditions": {
+                "Modality": "(?i)CT",
+                "ImageType": ["PRIMARY", "ORIGINAL"],
+                "PhotometricInterpretation": "MONOCHROME2"
+            }
+        }
+    ]
+}
+"""
+
 
 @resource(cpu=1, gpu=1, memory="7Gi")
 # pip_packages can be a string that is a path(str) to requirements.txt file or a list of packages.
@@ -46,7 +67,7 @@ def compose(self):
         self._logger.debug(f"Begin {self.compose.__name__}")
         # Creates the custom operator(s) as well as SDK built-in operator(s).
         study_loader_op = DICOMDataLoaderOperator()
-        series_selector_op = DICOMSeriesSelectorOperator()
+        series_selector_op = DICOMSeriesSelectorOperator(rules=Sample_Rules_Text)
         series_to_vol_op = DICOMSeriesToVolumeOperator()
         # Model specific inference operator, supporting MONAI transforms.
         liver_tumor_seg_op = LiverTumorSegOperator()
diff --git a/examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py b/examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py
index d2fccf67..6b5e41c8 100644
--- a/examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py
+++ b/examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py
@@ -34,7 +34,7 @@
 @md.input("image", Image, IOType.IN_MEMORY)
 @md.output("seg_image", Image, IOType.IN_MEMORY)
 @md.output("saved_images_folder", DataPath, IOType.DISK)
-@md.env(pip_packages=["monai>=0.8.1", "torch>=1.5", "numpy>=1.21", "nibabel"])
+@md.env(pip_packages=["monai==0.9.0", "torch>=1.5", "numpy>=1.21", "nibabel"])
 class LiverTumorSegOperator(Operator):
     """Performs liver and tumor segmentation using a DL model with an image converted from a DICOM CT series.
 
diff --git a/monai/deploy/operators/dicom_series_selector_operator.py b/monai/deploy/operators/dicom_series_selector_operator.py
index 54efa671..d088e48a 100644
--- a/monai/deploy/operators/dicom_series_selector_operator.py
+++ b/monai/deploy/operators/dicom_series_selector_operator.py
@@ -219,7 +219,17 @@ def _select_series(self, attributes: dict, study: DICOMStudy, all_matched=False)
                     continue
                 # Try getting the attribute value from Study and current Series prop dict
                 attr_value = series_attr.get(key, None)
-                logging.info(f"    Series attribute value: {attr_value}")
+                logging.info(f"    Series attribute {key} value: {attr_value}")
+
+                # If not found, try the best at the native instance level for string VR
+                # This is mainly for attributes like ImageType
+                if not attr_value:
+                    try:
+                        attr_value = [series.get_sop_instances()[0].get_native_sop_instance()[key].repval]
+                        series_attr.update({key: attr_value})
+                    except Exception:
+                        logging.info(f"        Attribute {key} not at instance level either.")
+
                 if not attr_value:
                     matched = False
                 elif isinstance(attr_value, numbers.Number):
@@ -233,12 +243,13 @@ def _select_series(self, attributes: dict, study: DICOMStudy, all_matched=False)
                         if re.search(value_to_match, attr_value, re.IGNORECASE):
                             matched = True
                 elif isinstance(attr_value, list):
-                    meta_data_set = {str(element).lower() for element in attr_value}
+                    # Assume multi value string attributes
+                    meta_data_list = str(attr_value).lower()
                     if isinstance(value_to_match, list):
                         value_set = {str(element).lower() for element in value_to_match}
-                        matched = all(val in meta_data_set for val in value_set)
+                        matched = all(val in meta_data_list for val in value_set)
                     elif isinstance(value_to_match, (str, numbers.Number)):
-                        matched = str(value_to_match).lower() in meta_data_set
+                        matched = str(value_to_match).lower() in meta_data_list
                 else:
                     raise NotImplementedError(f"Not support for matching on this type: {type(value_to_match)}")
 
diff --git a/setup.cfg b/setup.cfg
index 361fd924..aec9f6c0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -42,7 +42,8 @@ max_line_length = 120
 ignore =
     E203,E305,E402,E501,E721,E741,F821,F841,F999,W503,W504,C408,E302,W291,E303,
     # N812 lowercase 'torch.nn.functional' imported as non lowercase 'F'
-    N812
+    N812,
+    B024 #abstract base class, but it has no abstract methods
 per_file_ignores =
     __init__.py: F401
     # Allow using camel case for variable/argument names for the sake of readability.