Implement highdicom seg operator (#327)

* Implement highdicom seg operator

Signed-off-by: Chris Bridge <chrisbridge44@googlemail.com>

* Formatting fixes

Signed-off-by: Chris Bridge <chrisbridge44@googlemail.com>

* Typing fixes

Signed-off-by: Chris Bridge <chrisbridge44@googlemail.com>

* Update the Spleen App to use HighDicom Seg Writer.

The app is still not compatible with monai v0.9.1 as the app testing
revealed that to its Invert transform failed resample the predicted
image back to input image spacings. Also, the new Seg Writer impl is
strict on DICOM attribute VR conformance, and would throw exception
when the input DICOM instances have non-conformant attribute VR values.

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

* Fix isort error for ordering of imports

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

* Update doc strings and comments for seg label and algorithm name and version

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

* Pin moani==0.9.0 for now as 0.9.1 causes issues. Also pydicom to 2.3.0

as the use of highdicom require pydicom>=2.3.0

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

* Updated apps that have multiple segments

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

* Found the few missing codes, so avoided use of generic "Organ"

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

Signed-off-by: Chris Bridge <chrisbridge44@googlemail.com>
Signed-off-by: mmelqin <mingmelvinq@nvidia.com>
Co-authored-by: mmelqin <mingmelvinq@nvidia.com>

Author: CPBridge

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__}")