license update + code optimizations

Signed-off-by: bluna301 <[email protected]>

Author: bluna301

examples/apps/cchmc_ped_abd_ct_seg_app/README.md

@@ -9,7 +9,7 @@ For questions, please feel free to contact Elan Somasundaram (Elanchezhian.Somas
 ## Unique Features
 
 Some unique features of this MAP pipeline include:
-- **Custom Inference Operator:** custom `AbdomenSegOperator` enables either PyTorch or TorchScript model loading as desired
+- **Custom Inference Operator:** custom `AbdomenSegOperator` enables either PyTorch or TorchScript model loading
 - **DICOM Secondary Capture Output:** custom `DICOMSecondaryCaptureWriterOperator` writes a DICOM SC with organ contours
 - **Output Filtering:** model produces Liver-Spleen-Pancreas segmentations, but seg visibility in the outputs (DICOM SEG, SC, SR) can be controlled in `app.py`
 - **MONAI Deploy Express MongoDB Write:** custom operators (`MongoDBEntryCreatorOperator` and `MongoDBWriterOperator`) allow for writing to the MongoDB database associated with MONAI Deploy Express
@@ -20,9 +20,18 @@ Several scripts have been compiled that quickly execute useful actions (such as
 ## Notes
 The DICOM Series selection criteria has been customized based on the model's training and CCHMC use cases. By default, Axial CT series with Slice Thickness between 3.0 - 5.0 mm (inclusive) will be selected for. 
 
-If PyTorch model loading is desired, please uncomment the "PyTorch Model Loading" section in the `AbdomenSegOperator`.
+If MongoDB writing is not desired, please comment out the relevant sections in `app.py` and the `AbdomenSegOperator`. 
 
-If MongoDB writing is desired, please uncomment the relevant sections in `app.py` and the `AbdomenSegOperator`. Please note that MongoDB connection values (username, password, and port) are the default values pulled from the v0.6.0 MONAI Deploy Express [.env](https://github.com/Project-MONAI/monai-deploy/blob/main/deploy/monai-deploy-express/.env) and [docker-compose.yaml](https://github.com/Project-MONAI/monai-deploy/blob/main/deploy/monai-deploy-express/docker-compose.yml) files; these default values are harcoded into the `MongoDBWriterOperator`. If your instance of MONAI Deploy Express has modified values for these fields, the `MongoDBWriterOperator` will need to be udpated accordingly.
+To execute the pipeline with MongoDB writing enabled, it is best to create a `.env` file that the `MongoDBWriterOperator` can load in. Below is an example `.env` file that follows the format outlined in this operator; note that these values are the default variable values as defined in the [.env](https://github.com/Project-MONAI/monai-deploy/blob/main/deploy/monai-deploy-express/.env) and [docker-compose.yaml](https://github.com/Project-MONAI/monai-deploy/blob/main/deploy/monai-deploy-express/docker-compose.yml) files of v0.6.0 of MONAI Deploy Express:
+
+```dotenv
+MONGODB_USERNAME=root
+MONGODB_PASSWORD=rootpassword
+MONGODB_PORT=27017
+MONGODB_IP_DOCKER=172.17.0.1 # default Docker bridge network IP
+```
+
+Prior to packaging into a MAP, the MongoDB credentials should be harcoded into the `MongoDBWriterOperator`.
 
 The MONAI Deploy Express MongoDB Docker container (`mdl-mongodb`) needs to be connected to the Docker bridge network in order for the MongoDB write to be successful. Executing the following command in a MONAI Deploy Express terminal will establish this connection:
 

examples/apps/cchmc_ped_abd_ct_seg_app/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/apps/cchmc_ped_abd_ct_seg_app/__main__.py

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/apps/cchmc_ped_abd_ct_seg_app/abdomen_seg_operator.py

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -13,12 +13,14 @@
 from pathlib import Path
 from typing import List
 
+import torch
 from numpy import float32, int16
 
 # import custom transforms from post_transforms.py
 from post_transforms import CalculateVolumeFromMaskd, ExtractVolumeToTextd, LabelToContourd, OverlayImageLabeld
 
-from monai.deploy.core import AppContext, Fragment, Operator, OperatorSpec
+import monai
+from monai.deploy.core import AppContext, Fragment, Model, Operator, OperatorSpec
 from monai.deploy.operators.monai_seg_inference_operator import InfererType, InMemImageReader, MonaiSegInferenceOperator
 from monai.transforms import (
     Activationsd,
@@ -36,11 +38,6 @@
     Spacingd,
 )
 
-# # PyTorch model pipeline dependencies
-# import torch
-# import monai
-# from monai.deploy.core import Model
-
 
 # this operator performs inference with the new version of the bundle
 class AbdomenSegOperator(Operator):
@@ -95,6 +92,48 @@ def _find_model_file_path(self, model_path: Path):
 
         raise ValueError(f"Model file not found in the provided path: {model_path}")
 
+    # load a PyTorch model and register it in app_context
+    def _load_pytorch_model(self):
+
+        _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        _kernel_size: tuple = (3, 3, 3, 3, 3, 3)
+        _strides: tuple = (1, 2, 2, 2, 2, (2, 2, 1))
+        _upsample_kernel_size: tuple = (2, 2, 2, 2, (2, 2, 1))
+
+        # create DynUNet model with the specified architecture parameters + move to computational device (GPU or CPU)
+        # parameters pulled from inference.yaml file of the MONAI bundle
+        model = monai.networks.nets.dynunet.DynUNet(
+            spatial_dims=3,
+            in_channels=1,
+            out_channels=4,
+            kernel_size=_kernel_size,
+            strides=_strides,
+            upsample_kernel_size=_upsample_kernel_size,
+            norm_name="INSTANCE",
+            deep_supervision=False,
+            res_block=True,
+        ).to(_device)
+
+        # load model state dictionary (i.e. mapping param names to tensors) via torch.load
+        # weights_only=True to avoid arbitrary code execution during unpickling
+        state_dict = torch.load(self.model_path, weights_only=True)
+
+        # assign loaded weights to model architecture via load_state_dict
+        model.load_state_dict(state_dict)
+
+        # set model in evaluation (inference) mode
+        model.eval()
+
+        # create a MONAI Model object to encapsulate the PyTorch model and metadata
+        loaded_model = Model(self.model_path, name="ped_abd_ct_seg")
+
+        # assign loaded PyTorch model as the predictor for the Model object
+        loaded_model.predictor = model
+
+        # register the loaded Model object in the application context so other operators can access it
+        # MonaiSegInferenceOperator uses _get_model method to load models; looks at app_context.models first
+        self.app_context.models = loaded_model
+
     def setup(self, spec: OperatorSpec):
         spec.input(self.input_name_image)
 
@@ -104,8 +143,8 @@ def setup(self, spec: OperatorSpec):
         # DICOM SR
         spec.output(self.output_name_text_dicom_sr)
 
-        # # MongoDB
-        # spec.output(self.output_name_text_mongodb)
+        # MongoDB
+        spec.output(self.output_name_text_mongodb)
 
         # DICOM SC
         spec.output(self.output_name_sc_path)
@@ -122,50 +161,14 @@ def compute(self, op_input, op_output, context):
         pre_transforms = self.pre_process(_reader)
         post_transforms = self.post_process(pre_transforms)
 
-        ##########
-
-        # # PyTorch Model Loading:
-
-        # _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        # _kernel_size: tuple = (3, 3, 3, 3, 3, 3)
-        # _strides: tuple = (1, 2, 2, 2, 2, (2, 2, 1))
-        # _upsample_kernel_size: tuple = (2, 2, 2, 2, (2, 2, 1))
-
-        # # create DynUNet model with the specified architecture parameters + move to computational device (GPU or CPU)
-        # # parameters pulled from inference.yaml file of the MONAI bundle
-        # model = monai.networks.nets.dynunet.DynUNet(
-        #     spatial_dims=3,
-        #     in_channels=1,
-        #     out_channels=4,
-        #     kernel_size=_kernel_size,
-        #     strides=_strides,
-        #     upsample_kernel_size=_upsample_kernel_size,
-        #     norm_name="INSTANCE",
-        #     deep_supervision=False,
-        #     res_block=True
-        # ).to(_device)
-
-        # # load model state dictionary (i.e. mapping param names to tensors) via torch.load
-        # # weights_only=True to avoid arbitrary code execution during unpickling
-        # state_dict = torch.load(self.model_path, weights_only=True)
-
-        # # assign loaded weights to model architecture via load_state_dict
-        # model.load_state_dict(state_dict)
-
-        # # set model in evaluation (inference) mode
-        # model.eval()
-
-        # # create a MONAI Model object to encapsulate the PyTorch model and metadata
-        # loaded_model = Model(self.model_path, name="ped_abd_ct_seg")
-
-        # # assign loaded PyTorch model as the predictor for the Model object
-        # loaded_model.predictor = model
-
-        # # register the loaded Model object in the application context so other operators can access it
-        # # MonaiSegInferenceOperator uses _get_model method to load models; looks at app_context.models first
-        # self.app_context.models = loaded_model
-
-        ##########
+        # if PyTorch model
+        if self.model_path.suffix.lower() == ".pt":
+            # load the PyTorch model
+            self._logger.info("PyTorch model detected")
+            self._load_pytorch_model()
+        # else, we have TorchScript model
+        else:
+            self._logger.info("TorchScript model detected")
 
         # delegates inference and saving output to the built-in operator.
         infer_operator = MonaiSegInferenceOperator(
@@ -202,8 +205,8 @@ def compute(self, op_input, op_output, context):
         # DICOM SR
         op_output.emit(result_text_dicom_sr, self.output_name_text_dicom_sr)
 
-        # # MongoDB
-        # op_output.emit(result_text_mongodb, self.output_name_text_mongodb)
+        # MongoDB
+        op_output.emit(result_text_mongodb, self.output_name_text_mongodb)
 
         # DICOM SC
         # temporary DICOM SC (w/o source DICOM metadata) saved in output_folder / temp directory

examples/apps/cchmc_ped_abd_ct_seg_app/app.py

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -18,28 +18,22 @@
 # custom DICOM Secondary Capture (SC) writer operator
 from dicom_sc_writer_operator import DICOMSCWriterOperator
 
+# custom MongoDB operators
+from mongodb_entry_creator_operator import MongoDBEntryCreatorOperator
+from mongodb_writer_operator import MongoDBWriterOperator
+
 # 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.conditions import CountCondition
 from monai.deploy.core import Application
-
-# DICOM operators
 from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
-
-# DICOM writer operators
 from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription
-
-# custom DICOMSeriesSelectorOperator
 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.dicom_text_sr_writer_operator import DICOMTextSRWriterOperator, EquipmentInfo, ModelInfo
 
-# # MongoDB operators
-# from mongodb_entry_creator_operator import MongoDBEntryCreatorOperator
-# from mongodb_writer_operator import MongoDBWriterOperator
-
 
 # inherit new Application class instance, AIAbdomenSegApp, from MONAI Application base class
 # base class provides support for chaining up operators and executing application
@@ -195,23 +189,16 @@ def compose(self):
             output_folder=app_output_path / "SC",
         )
 
-        # # MongoDB database, collection, and MAP version info
-        # database_name = "CTLiverSpleenSegPredictions"
-        # collection_name = "OrganVolumes"
-        # map_version = "1.0.0"
+        # MongoDB database, collection, and MAP version info
+        database_name = "CTLiverSpleenSegPredictions"
+        collection_name = "OrganVolumes"
+        map_version = "1.0.0"
 
-        # # custom MongoDB Entry Creator op
-        # mongodb_entry_creator = MongoDBEntryCreatorOperator(
-        #     self,
-        #     map_version=map_version
-        # )
+        # custom MongoDB Entry Creator op
+        mongodb_entry_creator = MongoDBEntryCreatorOperator(self, map_version=map_version)
 
-        # # custom MongoDB Writer op
-        # mongodb_writer = MongoDBWriterOperator(
-        #     self,
-        #     database_name=database_name,
-        #     collection_name=collection_name
-        # )
+        # custom MongoDB Writer op
+        mongodb_writer = MongoDBWriterOperator(self, database_name=database_name, collection_name=collection_name)
 
         # 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
@@ -243,10 +230,12 @@ def compose(self):
         )
         self.add_flow(abd_seg_op, dicom_sc_writer, {("dicom_sc_dir", "dicom_sc_dir")})
 
-        # # MongoDB
-        # self.add_flow(series_selector_op, mongodb_entry_creator, {("study_selected_series_list", "study_selected_series_list")})
-        # self.add_flow(abd_seg_op, mongodb_entry_creator, {("result_text_mongodb", "text")})
-        # self.add_flow(mongodb_entry_creator, mongodb_writer, {("mongodb_database_entry", "mongodb_database_entry")})
+        # MongoDB
+        self.add_flow(
+            series_selector_op, mongodb_entry_creator, {("study_selected_series_list", "study_selected_series_list")}
+        )
+        self.add_flow(abd_seg_op, mongodb_entry_creator, {("result_text_mongodb", "text")})
+        self.add_flow(mongodb_entry_creator, mongodb_writer, {("mongodb_database_entry", "mongodb_database_entry")})
 
         logging.info(f"End {self.compose.__name__}")
 

examples/apps/cchmc_ped_abd_ct_seg_app/app.yaml

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/apps/cchmc_ped_abd_ct_seg_app/dicom_sc_writer_operator.py

@@ -1,4 +1,4 @@
-# Copyright 2021-2024 MONAI Consortium
+# Copyright 2021-2025 MONAI Consortium
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -197,13 +197,8 @@ def write(self, dicom_sc_dir, dicom_series: DICOMSeries, output_dir: Path):
 
         output_dir.mkdir(parents=True, exist_ok=True)  # just in case
 
-        # find the temporary DICOM SC file in the directory (assuming there's only one .dcm file)
+        # find the temporary DICOM SC file in the directory; there should only be one .dcm file present
         dicom_files = list(dicom_sc_dir.glob("*.dcm"))
-        if len(dicom_files) == 0:
-            raise FileNotFoundError(f"No DICOM files found in the directory: {dicom_sc_dir}")
-        if len(dicom_files) > 1:
-            self.logger.warning(f"Multiple DICOM files found, using the first one: {dicom_files[0]}")
-
         dicom_sc_file = dicom_files[0]
 
         # load the temporary DICOM SC file using pydicom