Add coronary artery segmentation bundle (CoronSegmentator)

ci/unit_tests/test_coronaryArtery_ct_segmentation.py

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+# Copyright (c) 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 os
+import shutil
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+from monai.bundle import ConfigWorkflow
+from parameterized import parameterized
+from utils import check_workflow
+
+ROOT = Path(__file__).parent.parent.parent
+bundle_path = os.path.join(ROOT, "models", "CoronSegmentator")
+
+if bundle_path not in sys.path:
+    sys.path.insert(0, bundle_path)
+    print(f"Added to sys.path: {bundle_path}")
+
+TEST_CASE_1 = [{"bundle_root": r"models\CoronSegmentator"}]  # inference
+
+
+def test_order(test_name1, test_name2):
+    def get_order(name):
+        if "train" in name:
+            return 1
+        if "eval" in name:
+            return 2
+        if "infer" in name:
+            return 3
+        return 4
+
+    return get_order(test_name1) - get_order(test_name2)
+
+
+class TestCoronaryArteryCTSeg(unittest.TestCase):
+    def setUp(self):
+        self.dataset_dir = tempfile.mkdtemp()
+        dataset_size = 10
+        input_shape = (128, 128, 128)
+        for s in range(dataset_size):
+            test_image = np.random.randint(low=-1024, high=1000, size=input_shape).astype(np.int8)
+            test_label = np.random.randint(low=0, high=1, size=input_shape).astype(np.int8)
+            image_filename = os.path.join(self.dataset_dir, f"image_{s}.nii.gz")
+            label_filename = os.path.join(self.dataset_dir, f"label_{s}.nii.gz")
+            nib.save(nib.Nifti1Image(test_image, np.eye(4)), image_filename)
+            nib.save(nib.Nifti1Image(test_label, np.eye(4)), label_filename)
+
+    def tearDown(self):
+        shutil.rmtree(self.dataset_dir)
+
+    @parameterized.expand([TEST_CASE_1])
+    def test_infer_config(self, override):
+        override["dataset_dir"] = self.dataset_dir
+        bundle_root = override["bundle_root"]
+
+        inferrer = ConfigWorkflow(
+            workflow_type="infer",
+            config_file=Path(bundle_root) / "configs/inference.json",
+            logging_file=Path(bundle_root) / "configs/logging.conf",
+            meta_file=Path(bundle_root) / "configs/metadata.json",
+            **override,
+        )
+        check_workflow(inferrer, check_properties=True)
+
+
+if __name__ == "__main__":
+    loader = unittest.TestLoader()
+    loader.sortTestMethodsUsing = test_order
+    unittest.main(testLoader=loader)

models/CoronSegmentator/.gitignore

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+*__pycache__
+*zip
+.python-version
+
+/env

models/CoronSegmentator/LICENSE

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models/CoronSegmentator/configs/inference.json

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+{
+    "imports": [
+        "$import glob"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "dataset_dir": "",
+    "data": "$list(sorted(glob.glob(@dataset_dir + '/*.nii.gz')))",
+    "output_ext": ".usd",
+    "output_dir": "$@bundle_root + '/Output'",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "inferer": {
+        "_target_": "scripts.heart_digital_twin.CoroSegmentatorPipeline",
+        "input_params": {
+            "inputFile": "$@data[0]",
+            "outputDir": "$@output_dir"
+        }
+    },
+    "dataset": {},
+    "evaluator": {},
+    "network_def": {},
+    "run": [
+        "[email protected]()"
+    ]
+}

models/CoronSegmentator/configs/logging.conf

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+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s