Use simple series selection rule on modality and image type

Signed-off-by: M Q <mingmelvinq@nvidia.com>

Author: MMelQin

examples/apps/breast_density_classifer_app/app.py

@@ -19,10 +19,10 @@ def compose(self):
             "0.1",
             "Center for Augmented Intelligence in Imaging, Mayo Clinic, Florida",
         )
-        my_equipment = EquipmentInfo(manufacturer="MONAI Deploy App SD", manufacturer_model="DICOM SR Writer")
+        my_equipment = EquipmentInfo(manufacturer="MONAI Deploy App SDK", manufacturer_model="DICOM SR Writer")
         my_special_tags = {"SeriesDescription": "Not for clinical use"}
         study_loader_op = DICOMDataLoaderOperator()
-        series_selector_op = DICOMSeriesSelectorOperator(rules="")
+        series_selector_op = DICOMSeriesSelectorOperator(rules=Sample_Rules_Text)
         series_to_vol_op = DICOMSeriesToVolumeOperator()
         classifier_op = ClassifierOperator()
         sr_writer_op = DICOMTextSRWriterOperator(
@@ -39,6 +39,27 @@ def compose(self):
         self.add_flow(series_selector_op, sr_writer_op, {"study_selected_series_list": "study_selected_series_list"})
 
 
+# This is a sample series selection rule in JSON, simply selecting a MG series.
+# If the study has more than 1 MG 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": "MG Series",
+            "conditions": {
+                "Modality": "(?i)MG",
+                "ImageType": ["PRIMARY"]
+            }
+        }
+    ]
+}
+"""
+
+
 def test():
     app = BreastClassificationApp()
     image_dir = "./sampleDICOMs/1/BI_BREAST_SCREENING_BILATERAL_WITH_TOMOSYNTHESIS-2019-07-08/1/L_CC_C-View"