Add support for configurable GD-enhancing tumor label in ConvertToMul… (#8779)

**Description**
This PR introduces an `et_label` parameter to
`ConvertToMultiChannelBasedOnBratsClasses` to easily support the BraTS
2023 dataset conventions without breaking backwards compatibility.

**Motivation and Context**
Historically (BraTS 18-21), the Enhancing Tumor (ET) label was 4.
However, starting with the BraTS 2023 Adult Glioma (GLI) dataset, the ET
label was shifted to 3. Because the original transform hardcoded the ET
label as 4, it fails to recognize the ET channel in newer datasets and
generates empty masks.

By parameterizing `et_label` and defaulting it to 4, existing pipelines
remain 100% unaffected, while BraTS 23 users can now simply pass
`et_label=3` to utilize the standard sub-region extraction (TC, WT, ET).

**Types of changes**

- [ ] Bug fix (non-breaking change which fixes an issue)
- [x] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)

---------

Signed-off-by: Abdessamad <abdoomis6@gmail.com>
Signed-off-by: Abdessamad Misdak <abdoomis6@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: 3 people

monai/transforms/utility/array.py

@@ -1049,19 +1049,34 @@ class ConvertToMultiChannelBasedOnBratsClasses(Transform):
     which include TC (Tumor core), WT (Whole tumor) and ET (Enhancing tumor):
     label 1 is the necrotic and non-enhancing tumor core, which should be counted under TC and WT subregion,
     label 2 is the peritumoral edema, which is counted only under WT subregion,
-    label 4 is the GD-enhancing tumor, which should be counted under ET, TC, WT subregions.
+    the specified `et_label` (default 4) is the GD-enhancing tumor, which should be counted under ET, TC, WT subregions.
+
+    Args:
+        et_label: the label used for the GD-enhancing tumor (ET).
+        - Use 4 for BraTS 2018-2022.
+        - Use 3 for BraTS 2023.
+        Defaults to 4.
     """
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
+    def __init__(self, et_label: int = 4) -> None:
+        if et_label in (1, 2):
+            raise ValueError(f"et_label cannot be 1 or 2, as these are reserved. Got {et_label}.")
+        self.et_label = et_label
+
     def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         # if img has channel dim, squeeze it
         if img.ndim == 4 and img.shape[0] == 1:
             img = img.squeeze(0)
 
-        result = [(img == 1) | (img == 4), (img == 1) | (img == 4) | (img == 2), img == 4]
-        # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
-        # label 4 is ET
+        result = [
+            (img == 1) | (img == self.et_label),
+            (img == 1) | (img == self.et_label) | (img == 2),
+            img == self.et_label,
+        ]
+        # merge labels 1 (tumor non-enh) and self.et_label (tumor enh) and 2 (large edema) to WT
+        # self.et_label is ET (4 or 3)
         return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)
 
 

monai/transforms/utility/dictionary.py

@@ -1297,19 +1297,27 @@ def __call__(self, data: Mapping[Hashable, Any]):
 class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.ConvertToMultiChannelBasedOnBratsClasses`.
-    Convert labels to multi channels based on brats18 classes:
+    Convert labels to multi channels based on brats classes:
     label 1 is the necrotic and non-enhancing tumor core
     label 2 is the peritumoral edema
-    label 4 is the GD-enhancing tumor
+    the specified `et_label` (default 4) is the GD-enhancing tumor
     The possible classes are TC (Tumor core), WT (Whole tumor)
     and ET (Enhancing tumor).
+
+    Args:
+        keys: keys of the corresponding items to be transformed.
+        et_label: the label used for the GD-enhancing tumor (ET).
+            - Use 4 for BraTS 2018-2022.
+            - Use 3 for BraTS 2023.
+            Defaults to 4.
+        allow_missing_keys: don't raise exception if key is missing.
     """
 
     backend = ConvertToMultiChannelBasedOnBratsClasses.backend
 
-    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False, et_label: int = 4):
         super().__init__(keys, allow_missing_keys)
-        self.converter = ConvertToMultiChannelBasedOnBratsClasses()
+        self.converter = ConvertToMultiChannelBasedOnBratsClasses(et_label=et_label)
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
         d = dict(data)

tests/transforms/test_convert_to_multi_channel.py

@@ -20,6 +20,9 @@
 from tests.test_utils import TEST_NDARRAYS, assert_allclose
 
 TESTS = []
+TESTS_ET_LABEL_3 = []
+
+# Tests for default et_label = 4
 for p in TEST_NDARRAYS:
     TESTS.extend(
         [
@@ -46,6 +49,23 @@
         ]
     )
 
+# Tests for et_label = 3
+for p in TEST_NDARRAYS:
+    TESTS_ET_LABEL_3.extend(
+        [
+            [
+                p([[0, 1, 2], [1, 2, 3], [0, 1, 3]]),
+                p(
+                    [
+                        [[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(TESTS)
@@ -54,6 +74,18 @@ def test_type_shape(self, data, expected_result):
         assert_allclose(result, expected_result)
         self.assertTrue(result.dtype in (bool, torch.bool))
 
+    @parameterized.expand(TESTS_ET_LABEL_3)
+    def test_type_shape_et_label_3(self, data, expected_result):
+        result = ConvertToMultiChannelBasedOnBratsClasses(et_label=3)(data)
+        assert_allclose(result, expected_result)
+        self.assertTrue(result.dtype in (bool, torch.bool))
+
+    def test_invalid_et_label(self):
+        with self.assertRaises(ValueError):
+            ConvertToMultiChannelBasedOnBratsClasses(et_label=1)
+        with self.assertRaises(ValueError):
+            ConvertToMultiChannelBasedOnBratsClasses(et_label=2)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/transforms/test_convert_to_multi_channeld.py

@@ -24,6 +24,12 @@
     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]]]),
 ]
 
+TEST_CASE_ET_LABEL_3 = [
+    {"keys": "label", "et_label": 3},
+    {"label": np.array([[0, 1, 2], [1, 2, 3], [0, 1, 3]])},
+    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):
 
@@ -32,6 +38,11 @@ def test_type_shape(self, keys, data, expected_result):
         result = ConvertToMultiChannelBasedOnBratsClassesd(**keys)(data)
         np.testing.assert_equal(result["label"], expected_result)
 
+    @parameterized.expand([TEST_CASE_ET_LABEL_3])
+    def test_et_label_3(self, keys, data, expected_result):
+        result = ConvertToMultiChannelBasedOnBratsClassesd(**keys)(data)
+        np.testing.assert_equal(result["label"], expected_result)
+
 
 if __name__ == "__main__":
     unittest.main()