CCHMC Ped Abd CT Seg Example App

docs/source/getting_started/examples.md

@@ -13,3 +13,4 @@
 - ai_unetr_seg_app
 - dicom_series_to_image_app
 - breast_density_classifer_app
+- cchmc_ped_abd_ct_seg_app

examples/apps/cchmc_ped_abd_ct_seg_app/README.md

@@ -0,0 +1,31 @@
+# MONAI Application Package (MAP) for CCHMC Pediatric Abdominal CT Segmentation MONAI Bundle
+
+This MAP is based on the [CCHMC Pediatric Abdominal CT Segmentation MONAI Bundle](https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle/tree/original). This model was developed at Cincinnati Children's Hospital Medical Center by the Department of Radiology.
+
+The PyTorch and TorchScript DynUNet models can be downloaded from the [MONAI Bundle Repository](https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle/tree/original/models).
+
+For questions, please feel free to contact Elan Somasundaram (Elanchezhian.Somasundaram@cchmc.org) and Bryan Luna (Bryan.Luna@cchmc.org).
+
+## Unique Features
+
+Some unique features of this MAP pipeline include:
+- **Custom Inference Operator:** custom `AbdomenSegOperator` enables either PyTorch or TorchScript model loading as desired
+- **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
+
+## Scripts
+Several scripts have been compiled that quickly execute useful actions (such as running the app code locally with Python interpreter, MAP packaging, MAP execution, etc.). Some scripts require the input of command line arguments; review the `scripts` folder for more details.
+
+## 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 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.
+
+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:
+
+```bash
+docker network connect bridge mdl-mongodb
+```

examples/apps/cchmc_ped_abd_ct_seg_app/__init__.py

@@ -0,0 +1,29 @@
+# Copyright 2021-2024 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
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# __init__.py is used to initialize a Python package
+# ensures that the directory __init__.py resides in is included at the start of the sys.path
+# this is useful when you want to import modules from this directory, even if it’s not the
+# directory where your Python script is running.
+
+# give access to operating system and Python interpreter
+import os
+import sys
+
+# grab absolute path of directory containing __init__.py
+_current_dir = os.path.abspath(os.path.dirname(__file__))
+
+# if sys.path is not the same as the directory containing the __init__.py file
+if sys.path and os.path.abspath(sys.path[0]) != _current_dir:
+    # insert directory containing __init__.py file at the beginning of sys.path
+    sys.path.insert(0, _current_dir)
+# delete variable
+del _current_dir

examples/apps/cchmc_ped_abd_ct_seg_app/__main__.py

@@ -0,0 +1,26 @@
+# Copyright 2021-2024 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
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# __main__.py is needed for MONAI Application Packager to detect the main app code (app.py) when
+# app.py is executed in the application folder path
+# e.g., python my_app
+
+import logging
+
+# import AIAbdomenSegApp class from app.py
+from app import AIAbdomenSegApp
+
+# if __main__.py is being run directly
+if __name__ == "__main__":
+    logging.info(f"Begin {__name__}")
+    # create and run an instance of AIAbdomenSegApp
+    AIAbdomenSegApp().run()
+    logging.info(f"End {__name__}")

examples/apps/cchmc_ped_abd_ct_seg_app/abdomen_seg_operator.py

@@ -0,0 +1,291 @@
+# Copyright 2021-2024 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
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from pathlib import Path
+from typing import List
+
+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
+from monai.deploy.operators.monai_seg_inference_operator import InfererType, InMemImageReader, MonaiSegInferenceOperator
+from monai.transforms import (
+    Activationsd,
+    AsDiscreted,
+    CastToTyped,
+    Compose,
+    CropForegroundd,
+    EnsureChannelFirstd,
+    EnsureTyped,
+    Invertd,
+    LoadImaged,
+    Orientationd,
+    SaveImaged,
+    ScaleIntensityRanged,
+    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):
+    """Performs segmentation inference with a custom model architecture."""
+
+    DEFAULT_OUTPUT_FOLDER = Path.cwd() / "output"
+
+    def __init__(
+        self,
+        fragment: Fragment,
+        *args,
+        app_context: AppContext,
+        model_path: Path,
+        output_folder: Path = DEFAULT_OUTPUT_FOLDER,
+        output_labels: List[int],
+        **kwargs,
+    ):
+
+        self._logger = logging.getLogger(f"{__name__}.{type(self).__name__}")
+        self._input_dataset_key = "image"
+        self._pred_dataset_key = "pred"
+
+        # self.model_path is compatible with TorchScript and PyTorch model workflows (pythonic and MAP)
+        self.model_path = self._find_model_file_path(model_path)
+
+        self.output_folder = output_folder
+        self.output_folder.mkdir(parents=True, exist_ok=True)
+        self.output_labels = output_labels
+        self.app_context = app_context
+        self.input_name_image = "image"
+        self.output_name_seg = "seg_image"
+        self.output_name_text_dicom_sr = "result_text_dicom_sr"
+        self.output_name_text_mongodb = "result_text_mongodb"
+        self.output_name_sc_path = "dicom_sc_dir"
+
+        # the base class has an attribute called fragment to hold the reference to the fragment object
+        super().__init__(fragment, *args, **kwargs)
+
+    # find model path; supports TorchScript and PyTorch model workflows (pythonic and MAP)
+    def _find_model_file_path(self, model_path: Path):
+        # when executing pythonically, model_path is a file
+        # when executing as MAP, model_path is a directory (/opt/holoscan/models)
+        #   torch.load() from PyTorch workflow needs file path; can't load model from directory
+        #   returns first found file in directory in this case
+        if model_path:
+            if model_path.is_file():
+                return model_path
+            elif model_path.is_dir():
+                for file in model_path.rglob("*"):
+                    if file.is_file():
+                        return file
+
+        raise ValueError(f"Model file not found in the provided path: {model_path}")
+
+    def setup(self, spec: OperatorSpec):
+        spec.input(self.input_name_image)
+
+        # DICOM SEG
+        spec.output(self.output_name_seg)
+
+        # DICOM SR
+        spec.output(self.output_name_text_dicom_sr)
+
+        # # MongoDB
+        # spec.output(self.output_name_text_mongodb)
+
+        # DICOM SC
+        spec.output(self.output_name_sc_path)
+
+    def compute(self, op_input, op_output, context):
+        input_image = op_input.receive(self.input_name_image)
+        if not input_image:
+            raise ValueError("Input image is not found.")
+
+        # this operator gets an in-memory Image object, so a specialized ImageReader is needed.
+        _reader = InMemImageReader(input_image)
+
+        # preprocessing and postprocessing
+        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
+
+        ##########
+
+        # delegates inference and saving output to the built-in operator.
+        infer_operator = MonaiSegInferenceOperator(
+            self.fragment,
+            roi_size=(96, 96, 96),
+            pre_transforms=pre_transforms,
+            post_transforms=post_transforms,
+            overlap=0.75,
+            app_context=self.app_context,
+            model_name="",
+            inferer=InfererType.SLIDING_WINDOW,
+            sw_batch_size=4,
+            model_path=self.model_path,
+            name="monai_seg_inference_op",
+        )
+
+        # setting the keys used in the dictionary-based transforms
+        infer_operator.input_dataset_key = self._input_dataset_key
+        infer_operator.pred_dataset_key = self._pred_dataset_key
+
+        seg_image = infer_operator.compute_impl(input_image, context)
+
+        # DICOM SEG
+        op_output.emit(seg_image, self.output_name_seg)
+
+        # grab result_text_dicom_sr and result_text_mongodb from ExractVolumeToTextd transform
+        result_text_dicom_sr, result_text_mongodb = self.get_result_text_from_transforms(post_transforms)
+        if not result_text_dicom_sr or not result_text_mongodb:
+            raise ValueError("Result text could not be generated.")
+
+        # only log volumes for target organs so logs reflect MAP behavior
+        self._logger.info(f"Calculated Organ Volumes: {result_text_dicom_sr}")
+
+        # 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)
+
+        # DICOM SC
+        # temporary DICOM SC (w/o source DICOM metadata) saved in output_folder / temp directory
+        dicom_sc_dir = self.output_folder / "temp"
+
+        self._logger.info(f"Temporary DICOM SC saved at: {dicom_sc_dir}")
+
+        op_output.emit(dicom_sc_dir, self.output_name_sc_path)
+
+    def pre_process(self, img_reader) -> Compose:
+        """Composes transforms for preprocessing the input image before predicting on a model."""
+
+        my_key = self._input_dataset_key
+
+        return Compose(
+            [
+                # img_reader: specialized InMemImageReader, derived from MONAI ImageReader
+                LoadImaged(keys=my_key, reader=img_reader),
+                EnsureChannelFirstd(keys=my_key),
+                Orientationd(keys=my_key, axcodes="RAS"),
+                Spacingd(keys=my_key, pixdim=[1.5, 1.5, 3.0], mode=["bilinear"]),
+                ScaleIntensityRanged(keys=my_key, a_min=-250, a_max=400, b_min=0.0, b_max=1.0, clip=True),
+                CropForegroundd(keys=my_key, source_key=my_key, mode="minimum"),
+                EnsureTyped(keys=my_key),
+                CastToTyped(keys=my_key, dtype=float32),
+            ]
+        )
+
+    def post_process(self, pre_transforms: Compose) -> Compose:
+        """Composes transforms for postprocessing the prediction results."""
+
+        pred_key = self._pred_dataset_key
+
+        labels = {"background": 0, "liver": 1, "spleen": 2, "pancreas": 3}
+
+        return Compose(
+            [
+                Activationsd(keys=pred_key, softmax=True),
+                Invertd(
+                    keys=[pred_key, self._input_dataset_key],
+                    transform=pre_transforms,
+                    orig_keys=[self._input_dataset_key, self._input_dataset_key],
+                    meta_key_postfix="meta_dict",
+                    nearest_interp=[False, False],
+                    to_tensor=True,
+                ),
+                AsDiscreted(keys=pred_key, argmax=True),
+                # custom post-processing steps
+                CalculateVolumeFromMaskd(keys=pred_key, label_names=labels),
+                # optional code for saving segmentation masks as a NIfTI
+                # SaveImaged(
+                #     keys=pred_key,
+                #     output_ext=".nii.gz",
+                #     output_dir=self.output_folder / "NIfTI",
+                #     meta_keys="pred_meta_dict",
+                #     separate_folder=False,
+                #     output_dtype=int16
+                # ),
+                # volume data stored in dictionary under pred_key + '_volumes' key
+                ExtractVolumeToTextd(
+                    keys=[pred_key + "_volumes"], label_names=labels, output_labels=self.output_labels
+                ),
+                # comment out LabelToContourd for seg masks instead of contours; organ filtering will be lost
+                LabelToContourd(keys=pred_key, output_labels=self.output_labels),
+                OverlayImageLabeld(image_key=self._input_dataset_key, label_key=pred_key, overlay_key="overlay"),
+                SaveImaged(
+                    keys="overlay",
+                    output_ext=".dcm",
+                    # save temporary DICOM SC (w/o source DICOM metadata) in output_folder / temp directory
+                    output_dir=self.output_folder / "temp",
+                    separate_folder=False,
+                    output_dtype=int16,
+                ),
+            ]
+        )
+
+    # grab volume data from ExtractVolumeToTextd transform
+    def get_result_text_from_transforms(self, post_transforms: Compose):
+        """Extracts the result_text variables from post-processing transforms output."""
+
+        # grab the result_text variables from ExractVolumeToTextd transfor
+        for transform in post_transforms.transforms:
+            if isinstance(transform, ExtractVolumeToTextd):
+                return transform.result_text_dicom_sr, transform.result_text_mongodb
+        return None