Refine Brats transform and add missing transforms docs

docs/source/transforms.rst

@@ -504,6 +504,18 @@ Utility
     :members:
     :special-members: __call__
 
+`ConvertToMultiChannelBasedOnBratsClasses`
+""""""""""""""""""""""""""""""""""""""""""
+.. autoclass:: ConvertToMultiChannelBasedOnBratsClasses
+    :members:
+    :special-members: __call__
+
+`AddExtremePointsChannel`
+"""""""""""""""""""""""""
+.. autoclass:: AddExtremePointsChannel
+    :members:
+    :special-members: __call__
+
 `TorchVision`
 """""""""""""
 .. autoclass:: TorchVision
@@ -975,6 +987,18 @@ Utility (Dict)
     :members:
     :special-members: __call__
 
+`ConvertToMultiChannelBasedOnBratsClassesd`
+"""""""""""""""""""""""""""""""""""""""""""
+.. autoclass:: ConvertToMultiChannelBasedOnBratsClassesd
+    :members:
+    :special-members: __call__
+
+`AddExtremePointsChanneld`
+""""""""""""""""""""""""""
+.. autoclass:: AddExtremePointsChanneld
+    :members:
+    :special-members: __call__
+
 `TorchVisiond`
 """"""""""""""
 .. autoclass:: TorchVisiond

monai/transforms/__init__.py

@@ -199,6 +199,7 @@
     AsChannelFirst,
     AsChannelLast,
     CastToType,
+    ConvertToMultiChannelBasedOnBratsClasses,
     DataStats,
     FgBgToIndices,
     Identity,

monai/transforms/utility/array.py

@@ -41,6 +41,7 @@
     "Lambda",
     "LabelToMask",
     "FgBgToIndices",
+    "ConvertToMultiChannelBasedOnBratsClasses",
     "AddExtremePointsChannel",
     "TorchVision",
 ]
@@ -556,6 +557,27 @@ def __call__(
         return fg_indices, bg_indices
 
 
+class ConvertToMultiChannelBasedOnBratsClasses(Transform):
+    """
+    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, img: np.ndarray) -> np.ndarray:
+        result = []
+        # merge labels 1 (tumor non-enh) and 4 (tumor enh) to TC
+        result.append(np.logical_or(img == 1, img == 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(img == 1, img == 4), img == 2))
+        # label 4 is ET
+        result.append(img == 4)
+        return np.stack(result, axis=0).astype(np.float32)
+
+
 class AddExtremePointsChannel(Transform, Randomizable):
     """
     Add extreme points of label to the image as a new channel. This transform generates extreme

monai/transforms/utility/dictionary.py

@@ -29,6 +29,7 @@
     AsChannelFirst,
     AsChannelLast,
     CastToType,
+    ConvertToMultiChannelBasedOnBratsClasses,
     DataStats,
     FgBgToIndices,
     Identity,
@@ -649,6 +650,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
 
 class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
     """
+    Dictionary-based wrapper of :py:class:`monai.transforms.ConvertToMultiChannelBasedOnBratsClasses`.
     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
@@ -657,17 +659,14 @@ class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
     and ET (Enhancing tumor).
     """
 
+    def __init__(self, keys: KeysCollection):
+        super().__init__(keys)
+        self.converter = ConvertToMultiChannelBasedOnBratsClasses()
+
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
         d = dict(data)
         for key in self.keys:
-            result = []
-            # 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)
+            d[key] = self.converter(d[key])
         return d
 
 

tests/test_convert_to_multi_channel.py

@@ -0,0 +1,33 @@
+# Copyright 2020 - 2021 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 ConvertToMultiChannelBasedOnBratsClasses
+
+TEST_CASE = [
+    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 TestConvertToMultiChannel(unittest.TestCase):
+    @parameterized.expand([TEST_CASE])
+    def test_type_shape(self, data, expected_result):
+        result = ConvertToMultiChannelBasedOnBratsClasses()(data)
+        np.testing.assert_equal(result, expected_result)
+
+
+if __name__ == "__main__":
+    unittest.main()