[DLMED] Enhance MaskIntensityd transform (#1521)

Signed-off-by: Nic Ma <[email protected]>

monai/transforms/intensity/dictionary.py

@@ -478,17 +478,26 @@ class MaskIntensityd(MapTransform):
             of input image. if multiple channels, the channel number must
             match input data. mask_data will be converted to `bool` values
             by `mask_data > 0` before applying transform to input image.
+            if None, will extract the mask data from input data based on `mask_key`.
+        mask_key: the key to extract mask data from input dictionary, only works
+            when `mask_data` is None.
 
     """
 
-    def __init__(self, keys: KeysCollection, mask_data: np.ndarray) -> None:
+    def __init__(
+        self,
+        keys: KeysCollection,
+        mask_data: Optional[np.ndarray] = None,
+        mask_key: Optional[str] = None,
+    ) -> None:
         super().__init__(keys)
         self.converter = MaskIntensity(mask_data)
+        self.mask_key = mask_key if mask_data is None else None
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
         d = dict(data)
         for key in self.keys:
-            d[key] = self.converter(d[key])
+            d[key] = self.converter(d[key], d[self.mask_key]) if self.mask_key is not None else self.converter(d[key])
         return d
 
 

tests/test_mask_intensityd.py

@@ -34,9 +34,18 @@
     np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),
 ]
 
+TEST_CASE_4 = [
+    {"keys": "img", "mask_key": "mask"},
+    {
+        "img": np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]]),
+        "mask": np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [0, 1, 0], [0, 1, 0]]]),
+    },
+    np.array([[[0, 0, 0], [0, 2, 0], [0, 0, 0]], [[0, 4, 0], [0, 5, 0], [0, 6, 0]]]),
+]
+
 
 class TestMaskIntensityd(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
     def test_value(self, argments, image, expected_data):
         result = MaskIntensityd(**argments)(image)
         np.testing.assert_allclose(result["img"], expected_data)