Add ConvertToMultiChannelBasedOnBratsClassesd

monai/transforms/utility/dictionary.py

@@ -636,6 +636,30 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
+class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
+    """
+    Convert labels to multi channels based on brats18 classes:
+    label 1 is the necrotic and non-enhancing tumor core
+    label 2 is the the peritumoral edema
+    label 4 is the GD-enhancing tumor
+    The possible classes are TC (Tumor core), WT (Whole tumor)
+    and ET (Enhancing tumor).
+    """
+
+    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
+        d = dict(data)
+        for key in self.keys:
+            result = list()
+            # merge labels 1 (tumor non-enh) and 4 (tumor enh) to TC
+            result.append(np.logical_or(d[key] == 1, d[key] == 4))
+            # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
+            result.append(np.logical_or(np.logical_or(d[key] == 1, d[key] == 4), d[key] == 2))
+            # label 4 is ET
+            result.append(d[key] == 4)
+            d[key] = np.stack(result, axis=0).astype(np.float32)
+        return d
+
+
 IdentityD = IdentityDict = Identityd
 AsChannelFirstD = AsChannelFirstDict = AsChannelFirstd
 AsChannelLastD = AsChannelLastDict = AsChannelLastd
@@ -653,3 +677,6 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
 LambdaD = LambdaDict = Lambdad
 LabelToMaskD = LabelToMaskDict = LabelToMaskd
 FgBgToIndicesD = FgBgToIndicesDict = FgBgToIndicesd
+ConvertToMultiChannelBasedOnBratsClassesD = (
+    ConvertToMultiChannelBasedOnBratsClassesDict
+) = ConvertToMultiChannelBasedOnBratsClassesd

tests/test_convert_to_multi_channeld.py

@@ -0,0 +1,34 @@
+# Copyright 2020 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 unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from monai.transforms import ConvertToMultiChannelBasedOnBratsClassesd
+
+TEST_CASE = [
+    {"keys": "label"},
+    {"label": np.array([[0, 1, 2], [1, 2, 4], [0, 1, 4]])},
+    np.array([[[0, 1, 0], [1, 0, 1], [0, 1, 1]], [[0, 1, 1], [1, 1, 1], [0, 1, 1]], [[0, 0, 0], [0, 0, 1], [0, 0, 1]]]),
+]
+
+
+class TestConvertToMultiChanneld(unittest.TestCase):
+    @parameterized.expand([TEST_CASE])
+    def test_type_shape(self, keys, data, expected_result):
+        result = ConvertToMultiChannelBasedOnBratsClassesd(**keys)(data)
+        np.testing.assert_equal(result["label"], expected_result)
+
+
+if __name__ == "__main__":
+    unittest.main()