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730 retinal oct rpd segmentation (#748)
Fixes # . ### Description This is a proposed segmentation model to add to the model zoo. ### Status Ready for review/work in progress. ### Please ensure all the checkboxes: <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Codeformat tests passed locally by running `./runtests.sh --codeformat`. - [ ] In-line docstrings updated. - [ ] Update `version` and `changelog` in `metadata.json` if changing an existing bundle. - [ x] Please ensure the naming rules in config files meet our requirements (please refer to: `CONTRIBUTING.md`). - [x ] Ensure versions of packages such as `monai`, `pytorch` and `numpy` are correct in `metadata.json`. - [x ] Descriptions should be consistent with the content, such as `eval_metrics` of the provided weights and TorchScript modules. - [x ] Files larger than 25MB are excluded and replaced by providing download links in `large_file.yml`. - [ x] Avoid using path that contains personal information within config files (such as use `/home/your_name/` for `"bundle_root"`). --------- Signed-off-by: Yiheng Wang <[email protected]> Co-authored-by: Yiheng Wang <[email protected]> Co-authored-by: Yiheng Wang <[email protected]>
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‎ci/bundle_custom_data.py‎

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@@ -23,11 +23,16 @@
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"maisi_ct_generative",
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"cxr_image_synthesis_latent_diffusion_model",
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"brain_image_synthesis_latent_diffusion_model",
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"retinalOCT_RPD_segmentation",
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]
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# This list is used for our CI tests to determine whether a bundle contains the preferred files.
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# If a bundle does not have any of the preferred files, please add the bundle name into the list.
30-
exclude_verify_preferred_files_list = ["pediatric_abdominal_ct_segmentation", "maisi_ct_generative"]
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exclude_verify_preferred_files_list = [
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"pediatric_abdominal_ct_segmentation",
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"maisi_ct_generative",
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"retinalOCT_RPD_segmentation",
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]
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# This list is used for our CI tests to determine whether a bundle needs to be tested with
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# the `verify_export_torchscript` function in `verify_bundle.py`.
@@ -47,6 +52,7 @@
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"mednist_ddpm",
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"cxr_image_synthesis_latent_diffusion_model",
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"brain_image_synthesis_latent_diffusion_model",
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"retinalOCT_RPD_segmentation",
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]
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# This list is used for our CI tests to determine whether a bundle needs to be tested after downloading
@@ -58,7 +64,9 @@
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# This dict is used for our CI tests to install required dependencies that cannot be installed by `pip install` directly.
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# If a bundle has this kind of dependencies, please add the bundle name (key), and the path of the install script (value)
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# into the dict.
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install_dependency_dict = {}
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install_dependency_dict = {
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"retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh"
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}
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# This list is used for our CI tests to determine whether a bundle supports TensorRT export. Related
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# test will be employed for bundles in the dict.

‎ci/get_bundle_requirements.py‎

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@@ -19,6 +19,8 @@
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ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes")
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special_dependencies_list = ["detectron2"]
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def increment_version(version):
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"""
@@ -79,6 +81,8 @@ def get_requirements(bundle, models_path, requirements_file):
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if package_key in metadata.keys():
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optional_dict = metadata[package_key]
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for name, version in optional_dict.items():
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if name in special_dependencies_list:
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continue
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libs.append(f"{name}=={version}")
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if len(libs) > 0:

‎ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh‎

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@@ -0,0 +1 @@
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python -m pip install 'git+https://github.com/facebookresearch/detectron2.git@65184fc057d4fab080a98564f6b60fae0b94edc4'

‎ci/unit_tests/test_retinalOCT_RPD_segmentation.py‎

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import glob
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import json
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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import unittest
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import pandas as pd
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import yaml
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class TestRPDInference(unittest.TestCase):
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def setUp(self):
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print(os.getcwd())
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# set the bundle root to the directory the test is being run from.
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self.bundle_root = os.path.abspath(
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os.path.join(os.path.dirname(__file__), "..", "..", "models", "retinalOCT_RPD_segmentation")
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)
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# Change the current working directory to bundle_root
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os.chdir(self.bundle_root)
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# Create a temporary directory for test data
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self.test_data_dir = tempfile.mkdtemp()
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self.extracted_dir = os.path.join(self.bundle_root, "sample_data")
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# create a dummy metadata.json file.
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metadata_file = os.path.join(self.test_data_dir, "metadata.json")
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metadata = {
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"version": "0.0.1",
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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}
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with open(metadata_file, "w") as f:
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json.dump(metadata, f)
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# create output directory.
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self.output_dir = os.path.join(self.test_data_dir, "output")
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os.makedirs(self.output_dir)
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def tearDown(self):
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# Clean up the temporary directory
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shutil.rmtree(self.test_data_dir)
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def test_inference_run(self):
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# Override configuration parameters
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override = {
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"args": {
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"extracted_dir": self.extracted_dir,
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"output_dir": self.output_dir,
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"run_extract": False,
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"create_dataset": True,
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"run_inference": True,
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"binary_mask": True,
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"binary_mask_overlay": True,
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"instance_mask_overlay": True,
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"dataset_name": "testDataset",
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}
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}
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# Load the original inference.yaml
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inference_yaml_path = "configs/inference.yaml"
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with open(inference_yaml_path, "r") as f:
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inference_yaml = yaml.safe_load(f)
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# Modify inference.yaml with override parameters.
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inference_yaml["args"].update(override["args"])
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# Create a new inference.yaml in the test_data_dir
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test_inference_yaml_path = os.path.join(self.test_data_dir, "inference.yaml")
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with open(test_inference_yaml_path, "w") as f:
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yaml.dump(inference_yaml, f)
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# Run the inference command using subprocess
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cmd = [
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sys.executable, # Use the same Python interpreter
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"-m",
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"monai.bundle",
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"run",
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"inference",
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"--bundle_root",
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self.bundle_root,
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"--config_file",
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test_inference_yaml_path, # Use the new file
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"--meta_file",
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os.path.join(self.test_data_dir, "metadata.json"),
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]
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try:
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subprocess.run(cmd, check=True)
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except subprocess.CalledProcessError as e:
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self.fail(f"Inference command failed: {e}")
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# Add assertions to check the output
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# Check if output files were created
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output_files = os.listdir(self.output_dir)
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self.assertTrue(len(output_files) > 0)
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# Check for the COCO JSON file
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coco_file_found = glob.glob(os.path.join(self.output_dir, "**", "coco_instances_results.json"), recursive=True)
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print(coco_file_found)
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self.assertTrue(len(coco_file_found) == 6)
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# Check for the TIFF files.
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tiff_files_found = glob.glob(os.path.join(self.output_dir, "**", "*.tiff"), recursive=True)
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self.assertTrue(len(tiff_files_found) == 6)
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# Check for the html files.
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html_files_found = glob.glob(os.path.join(self.output_dir, "*.html"))
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self.assertTrue(len(html_files_found) == 2)
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# At least 10 RPD present in sample data
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dfvol = pd.read_html(os.path.join(self.output_dir, "dfvol_testDataset.html"))[0]
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self.assertTrue(dfvol["dt_instances"].sum().iloc[0] > 10)
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if __name__ == "__main__":
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unittest.main()

‎models/retinalOCT_RPD_segmentation/LICENSE‎

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BSD 2-Clause License
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Copyright (c) 2022, uw-biomedical-ml
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎models/retinalOCT_RPD_segmentation/configs/inference.yaml‎

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imports:
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- $import scripts
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- $import scripts.inference
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args:
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run_extract : False
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input_dir : "/path/to/data"
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extracted_dir : "/path/to/extracted/data"
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input_format : "dicom"
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create_dataset : True
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dataset_name : "my_dataset_name"
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output_dir : "/path/to/model/output"
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run_inference : True
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create_tables : True
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# create visuals
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binary_mask : False
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binary_mask_overlay : True
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instance_mask_overlay : False
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inference:
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- $scripts.inference.main(@args)

‎models/retinalOCT_RPD_segmentation/configs/metadata.json‎

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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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"version": "0.0.1",
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"changelog": {
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"0.0.1": "Initial version"
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},
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"monai_version": "1.5.0",
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"pytorch_version": "2.6.0",
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"numpy_version": "1.26.4",
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"optional_packages_version": {},
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"required_packages_version": {
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"setuptools": "75.8.0",
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"opencv-python-headless": "4.11.0.86",
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"pandas": "2.3.0",
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"seaborn": "0.13.2",
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"scikit-learn": "1.6.1",
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"progressbar": "2.5",
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"pydicom": "3.0.1",
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"fire": "0.7.0",
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"torchvision": "0.21.0",
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"detectron2": "0.6",
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"lxml": "5.4.0",
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"pillow": "11.2.1"
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},
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"name": "retinalOCT_RPD_segmentation",
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"task": "Reticular Pseudodrusen (RPD) instance segmentation.",
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"description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.",
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"authors": "Yelena Bagdasarova, Scott Song",
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"copyright": "Copyright (c) 2022, uw-biomedical-ml",
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"network_data_format": {
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"inputs": {
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"image": {
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"type": "image",
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"format": "magnitude",
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"modality": "OCT",
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"num_channels": 1,
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"spatial_shape": [
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496,
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1024
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],
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"dtype": "int16",
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"value_range": [
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0,
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256
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],
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"is_patch_data": false,
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"channel_def": {
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"0": "image"
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}
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}
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},
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"preprocessed_data_sources": {
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"vol_file": {
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"type": "image",
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"format": "magnitude",
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"modality": "OCT",
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"num_channels": 1,
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"spatial_shape": [
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496,
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1024,
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"D"
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],
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"dtype": "int16",
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"value_range": [
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0,
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256
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],
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"description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to <extracted_dir>/<some>/<file>/<name>/<some_file_name>_oct_<DDD>.png on disk, where <DDD> is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. "
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},
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"dicom_series": {
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"type": "image",
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"format": "magnitude",
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"modality": "OCT",
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"SOP class UID": "1.2.840.10008.5.1.4.1.1.77.1.5.4",
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"num_channels": 1,
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"spatial_shape": [
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496,
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1024,
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"D"
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],
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"dtype": "int16",
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"value_range": [
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0,
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256
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],
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"description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to <extracted_dir>/<SOPInstanceUID>/<SOPInstanceUID>_oct_<DDD>.png on disk, where <DDD> is the slice number. "
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}
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},
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"outputs": {
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"pred": {
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"dtype": "dictionary",
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"type": "dictionary",
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"format": "COCO",
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"modality": "n/a",
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"value_range": [
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0,
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1
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],
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"num_channels": 1,
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"spatial_shape": [
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496,
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1024
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],
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"channel_def": {
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"0": "RPD"
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},
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"description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances."
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}
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},
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"post_processed_outputs": {
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"binary segmentation": {
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"type": "image",
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"format": "TIFF",
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"modality": "OCT",
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"num_channels": 3,
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"spatial_shape": [
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496,
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1024
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],
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"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices."
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},
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"binary segmentation overlay": {
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"type": "image",
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"format": "TIFF",
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"modality": "OCT",
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"num_channels": 3,
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"spatial_shape": [
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496,
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1024
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],
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"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask."
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},
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"instance segmentation overlay": {
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"type": "image",
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"format": "TIFF",
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"modality": "OCT",
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"num_channels": 3,
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"spatial_shape": [
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496,
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1024
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],
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"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask."
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}
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}
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}
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}

‎models/retinalOCT_RPD_segmentation/docs/Figure1.jpg‎

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