Implement highdicom seg operator

examples/apps/ai_livertumor_seg_app/app.py

@@ -13,9 +13,12 @@
 
 from livertumor_seg_operator import LiverTumorSegOperator
 
+# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.
+from pydicom.sr.codedict import codes
+
 from monai.deploy.core import Application, resource
 from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
-from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator
+from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
 from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator
 from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
 from monai.deploy.operators.publisher_operator import PublisherOperator
@@ -46,33 +49,57 @@ def compose(self):
         series_selector_op = DICOMSeriesSelectorOperator()
         series_to_vol_op = DICOMSeriesToVolumeOperator()
         # Model specific inference operator, supporting MONAI transforms.
-        unetr_seg_op = LiverTumorSegOperator()
+        liver_tumor_seg_op = LiverTumorSegOperator()
 
         # Create the publisher operator
         publisher_op = PublisherOperator()
 
-        # Creates DICOM Seg writer with segment label name in a string list
-        dicom_seg_writer = DICOMSegmentationWriterOperator(
-            seg_labels=[
-                "Liver",
-                "Tumor",
-            ]
-        )
+        # Create DICOM Seg writer providing the required segment description for each segment with
+        # the actual algorithm and the pertinent organ/tissue.
+        # The segment_label, algorithm_name, and algorithm_version are limited to 64 chars.
+        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html
+        # User can Look up SNOMED CT codes at, e.g.
+        # https://bioportal.bioontology.org/ontologies/SNOMEDCT
+
+        _algorithm_name = "3D segmentation of the liver and tumor from CT image"
+        _algorithm_family = codes.DCM.ArtificialIntelligence
+        _algorithm_version = "0.1.0"
+
+        segment_descriptions = [
+            SegmentDescription(
+                segment_label="Liver",
+                segmented_property_category=codes.SCT.Organ,
+                segmented_property_type=codes.SCT.Liver,
+                algorithm_name=_algorithm_name,
+                algorithm_family=_algorithm_family,
+                algorithm_version=_algorithm_version,
+            ),
+            SegmentDescription(
+                segment_label="Tumor",
+                segmented_property_category=codes.SCT.Tumor,
+                segmented_property_type=codes.SCT.Tumor,
+                algorithm_name=_algorithm_name,
+                algorithm_family=_algorithm_family,
+                algorithm_version=_algorithm_version,
+            ),
+        ]
+
+        dicom_seg_writer = DICOMSegmentationWriterOperator(segment_descriptions)
         # Create the processing pipeline, by specifying the source and destination operators, and
         # ensuring the output from the former matches the input of the latter, in both name and type.
         self.add_flow(study_loader_op, series_selector_op, {"dicom_study_list": "dicom_study_list"})
         self.add_flow(
             series_selector_op, series_to_vol_op, {"study_selected_series_list": "study_selected_series_list"}
         )
-        self.add_flow(series_to_vol_op, unetr_seg_op, {"image": "image"})
+        self.add_flow(series_to_vol_op, liver_tumor_seg_op, {"image": "image"})
         # Add the publishing operator to save the input and seg images for Render Server.
         # Note the PublisherOperator has temp impl till a proper rendering module is created.
-        self.add_flow(unetr_seg_op, publisher_op, {"saved_images_folder": "saved_images_folder"})
+        self.add_flow(liver_tumor_seg_op, publisher_op, {"saved_images_folder": "saved_images_folder"})
         # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.
         self.add_flow(
             series_selector_op, dicom_seg_writer, {"study_selected_series_list": "study_selected_series_list"}
         )
-        self.add_flow(unetr_seg_op, dicom_seg_writer, {"seg_image": "seg_image"})
+        self.add_flow(liver_tumor_seg_op, dicom_seg_writer, {"seg_image": "seg_image"})
 
         self._logger.debug(f"End {self.compose.__name__}")
 

examples/apps/ai_spleen_seg_app/app.py

@@ -11,11 +11,14 @@
 
 import logging
 
+# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.
+from pydicom.sr.codedict import codes
+
 from monai.deploy.core import Application, resource
 from monai.deploy.core.domain import Image
 from monai.deploy.core.io_type import IOType
 from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
-from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator
+from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
 from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator
 from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
 from monai.deploy.operators.monai_bundle_inference_operator import IOMapping, MonaiBundleInferenceOperator
@@ -56,13 +59,29 @@ def compose(self):
         # during init to provide the optional packages info, parsed from the bundle, to the packager
         # for it to install the packages in the MAP docker image.
         # Setting output IOType to DISK only works only for leaf operators, not the case in this example.
+        #
+        # Pertinent MONAI Bundle:
+        #   https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation
         bundle_spleen_seg_op = MonaiBundleInferenceOperator(
             input_mapping=[IOMapping("image", Image, IOType.IN_MEMORY)],
             output_mapping=[IOMapping("pred", Image, IOType.IN_MEMORY)],
         )
 
-        # Create DICOM Seg writer with segment label name in a string list
-        dicom_seg_writer = DICOMSegmentationWriterOperator(seg_labels=["Spleen"])
+        # Create DICOM Seg writer providing the required segment description for each segment with
+        # the actual algorithm and the pertinent organ/tissue. The segment_label, algorithm_name,
+        # and algorithm_version are of DICOM VR LO type, limited to 64 chars.
+        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html
+        segment_descriptions = [
+            SegmentDescription(
+                segment_label="Spleen",
+                segmented_property_category=codes.SCT.Organ,
+                segmented_property_type=codes.SCT.Spleen,
+                algorithm_name="volumetric (3D) segmentation of the spleen from CT image",
+                algorithm_family=codes.DCM.ArtificialIntelligence,
+                algorithm_version="0.1.0",
+            )
+        ]
+        dicom_seg_writer = DICOMSegmentationWriterOperator(segment_descriptions=segment_descriptions)
 
         # Create the processing pipeline, by specifying the source and destination operators, and
         # ensuring the output from the former matches the input of the latter, in both name and type.

examples/apps/ai_unetr_seg_app/app.py

@@ -11,10 +11,13 @@
 
 import logging
 
+# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package.
+from pydicom.sr.codedict import codes
 from unetr_seg_operator import UnetrSegOperator
 
 from monai.deploy.core import Application, resource
 from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
+from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
 from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator
 from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
 from monai.deploy.operators.publisher_operator import PublisherOperator
@@ -54,6 +57,50 @@ def compose(self):
             output_file="stl/multi-organs.stl", keep_largest_connected_component=False
         )
 
+        # Create DICOM Seg writer providing the required segment description for each segment with
+        # the actual algorithm and the pertinent organ/tissue.
+        # The segment_label, algorithm_name, and algorithm_version are limited to 64 chars.
+        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html
+
+        _algorithm_name = "3D multi-organ segmentation from CT image"
+        _algorithm_family = codes.DCM.ArtificialIntelligence
+        _algorithm_version = "0.1.0"
+
+        # List of (Segment name, [Code menaing str]), not including background which is value of 0.
+        # User must provide correct codes, which can be looked at, e.g.
+        # https://bioportal.bioontology.org/ontologies/SNOMEDCT
+        # Alternatively, consult the concept and code dictionaries in PyDicom
+
+        organs = [
+            ("Spleen",),
+            ("Right Kidney", "Kidney"),
+            ("Left Kideny", "Kidney"),
+            ("Gallbladder",),
+            ("Esophagus",),
+            ("Liver",),
+            ("Stomach",),
+            ("Aorta",),
+            ("Inferior vena cava", "InferiorVenaCava"),
+            ("Portal and Splenic Veins", "SplenicVein"),
+            ("Pancreas",),
+            ("Right adrenal gland", "AdrenalGland"),
+            ("Left adrenal gland", "AdrenalGland"),
+        ]
+
+        segment_descriptions = [
+            SegmentDescription(
+                segment_label=organ[0],
+                segmented_property_category=codes.SCT.Organ,
+                segmented_property_type=codes.SCT.__getattr__(organ[1] if len(organ) > 1 else organ[0]),
+                algorithm_name=_algorithm_name,
+                algorithm_family=_algorithm_family,
+                algorithm_version=_algorithm_version,
+            )
+            for organ in organs
+        ]
+
+        dicom_seg_writer = DICOMSegmentationWriterOperator(segment_descriptions)
+
         # Create the processing pipeline, by specifying the source and destination operators, and
         # ensuring the output from the former matches the input of the latter, in both name and type.
         self.add_flow(study_loader_op, series_selector_op, {"dicom_study_list": "dicom_study_list"})
@@ -67,6 +114,12 @@ def compose(self):
         # Note the PublisherOperator has temp impl till a proper rendering module is created.
         self.add_flow(unetr_seg_op, publisher_op, {"saved_images_folder": "saved_images_folder"})
 
+        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.
+        self.add_flow(
+            series_selector_op, dicom_seg_writer, {"study_selected_series_list": "study_selected_series_list"}
+        )
+        self.add_flow(unetr_seg_op, dicom_seg_writer, {"seg_image": "seg_image"})
+
         self._logger.debug(f"End {self.compose.__name__}")