Rename NormalizeLabelsInDatasetd to RemapLabelsToSequentiald and fix label ordering bug

monai/apps/deepedit/transforms.py

@@ -24,7 +24,7 @@
 from monai.data import MetaTensor
 from monai.networks.layers import GaussianFilter
 from monai.transforms.transform import MapTransform, Randomizable, Transform
-from monai.utils import min_version, optional_import
+from monai.utils import deprecated, min_version, optional_import
 
 measure, _ = optional_import("skimage.measure", "0.14.2", min_version)
 
@@ -84,18 +84,44 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
         return d
 
 
-class NormalizeLabelsInDatasetd(MapTransform):
+class RemapLabelsToSequentiald(MapTransform):
+    """
+    Remap label values from a dataset-specific schema to sequential indices (0, 1, 2, 3, ...).
+
+    This transform takes labels with arbitrary values defined in a label dictionary and remaps them
+    to a sequential range starting from 1 (with background always set to 0). This is useful for
+    standardizing labels across different datasets or ensuring labels are in a contiguous range.
+
+    The output label indices are assigned in alphabetical order by label name to ensure
+    deterministic behavior regardless of input dictionary ordering.
+
+    Args:
+        keys: The ``keys`` parameter will be used to get and set the actual data item to transform
+        label_names: Dictionary mapping label names to their current values in the dataset.
+            For example: {"spleen": 1, "liver": 6, "background": 0}
+            Will be remapped to: {"background": 0, "liver": 1, "spleen": 2}
+            (alphabetically sorted, excluding background)
+        allow_missing_keys: If True, missing keys in the data dictionary will not raise an error
+
+    Example:
+        >>> transform = RemapLabelsToSequentiald(
+        ...     keys="label",
+        ...     label_names={"liver": 6, "spleen": 1, "background": 0}
+        ... )
+        >>> # Input label has values [0, 1, 6]
+        >>> # Output label will have values [0, 1, 2] (background=0, liver=1, spleen=2)
+        >>> # And updates d["label_names"] to {"background": 0, "liver": 1, "spleen": 2}
+
+    Note:
+        - Background label (if present) is always mapped to 0
+        - Non-background labels are mapped to sequential indices 1, 2, 3, ... in alphabetical order
+        - Undefined labels (not in label_names) will be set to 0 (background)
+        - The transform updates the data dictionary with a new "label_names" key containing the remapped values
+    """
 
     def __init__(
         self, keys: KeysCollection, label_names: dict[str, int] | None = None, allow_missing_keys: bool = False
     ):
-        """
-        Normalize label values according to label names dictionary
-
-        Args:
-            keys: The ``keys`` parameter will be used to get and set the actual data item to transform
-            label_names: all label names
-        """
         super().__init__(keys, allow_missing_keys)
 
         self.label_names = label_names or {}
@@ -106,13 +132,18 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
             # Dictionary containing new label numbers
             new_label_names = {}
             label = np.zeros(d[key].shape)
-            # Making sure the range values and number of labels are the same
-            for idx, (key_label, val_label) in enumerate(self.label_names.items(), start=1):
-                if key_label != "background":
-                    new_label_names[key_label] = idx
-                    label[d[key] == val_label] = idx
-                if key_label == "background":
-                    new_label_names["background"] = 0
+
+            # Sort label names to ensure deterministic ordering (exclude background)
+            sorted_labels = sorted([(k, v) for k, v in self.label_names.items() if k != "background"])
+
+            # Always set background to 0 first
+            if "background" in self.label_names:
+                new_label_names["background"] = 0
+
+            # Assign sequential indices to sorted non-background labels
+            for idx, (key_label, val_label) in enumerate(sorted_labels, start=1):
+                new_label_names[key_label] = idx
+                label[d[key] == val_label] = idx
 
             d["label_names"] = new_label_names
             if isinstance(d[key], MetaTensor):
@@ -122,6 +153,20 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
         return d
 
 
+@deprecated(since="1.6", removed="1.8", msg_suffix="Use `RemapLabelsToSequentiald` instead.")
+class NormalizeLabelsInDatasetd(RemapLabelsToSequentiald):
+    """
+    .. deprecated:: 1.6.0
+        `NormalizeLabelsInDatasetd` is deprecated and will be removed in version 1.8.0.
+        Use :class:`RemapLabelsToSequentiald` instead.
+
+    This class is maintained for backward compatibility. Please use RemapLabelsToSequentiald
+    which better describes the transform's functionality.
+    """
+
+    pass
+
+
 class SingleLabelSelectiond(MapTransform):
 
     def __init__(

tests/apps/deepedit/test_deepedit_transforms.py

@@ -25,6 +25,7 @@
     FindAllValidSlicesMissingLabelsd,
     FindDiscrepancyRegionsDeepEditd,
     NormalizeLabelsInDatasetd,
+    RemapLabelsToSequentiald,
     ResizeGuidanceMultipleLabelDeepEditd,
     SingleLabelSelectiond,
     SplitPredsLabeld,
@@ -282,6 +283,100 @@ def test_correct_results(self, arguments, input_data, expected_result):
         result = add_fn(input_data)
         self.assertEqual(len(np.unique(result["label"])), expected_result)
 
+    def test_ordering_determinism(self):
+        """Test that different input ordering produces the same output (alphabetical)"""
+        # Create a label array with different label values
+        label = np.array([[[0, 1, 6, 3]]])  # background=0, spleen=1, liver=6, kidney=3
+
+        # Test case 1: liver first, then kidney, then spleen
+        data1 = {"label": label.copy()}
+        transform1 = RemapLabelsToSequentiald(
+            keys="label", label_names={"liver": 6, "kidney": 3, "spleen": 1, "background": 0}
+        )
+        result1 = transform1(data1)
+
+        # Test case 2: spleen first, then kidney, then liver (different order)
+        data2 = {"label": label.copy()}
+        transform2 = RemapLabelsToSequentiald(
+            keys="label", label_names={"spleen": 1, "kidney": 3, "liver": 6, "background": 0}
+        )
+        result2 = transform2(data2)
+
+        # Both should produce the same output (alphabetically sorted)
+        # Expected mapping: background=0, kidney=1, liver=2, spleen=3
+        np.testing.assert_array_equal(result1["label"], result2["label"])
+
+        # Verify the actual mapping is alphabetical
+        expected_output = np.array([[[0, 3, 2, 1]]])  # kidney=1, liver=2, spleen=3, background=0
+        np.testing.assert_array_equal(result1["label"], expected_output)
+
+        # Verify label_names is correct
+        self.assertEqual(result1["label_names"], {"background": 0, "kidney": 1, "liver": 2, "spleen": 3})
+        self.assertEqual(result2["label_names"], {"background": 0, "kidney": 1, "liver": 2, "spleen": 3})
+
+    def test_multiple_labels(self):
+        """Test with multiple non-background labels"""
+        label = np.array([[[0, 1, 2, 5]]])  # background, spleen, kidney, liver
+        data = {"label": label.copy()}
+        transform = RemapLabelsToSequentiald(
+            keys="label", label_names={"spleen": 1, "kidney": 2, "liver": 5, "background": 0}
+        )
+        result = transform(data)
+
+        # Expected: background=0, kidney=1, liver=2, spleen=3 (alphabetical)
+        expected = np.array([[[0, 3, 1, 2]]])
+        np.testing.assert_array_equal(result["label"], expected)
+        self.assertEqual(result["label_names"], {"background": 0, "kidney": 1, "liver": 2, "spleen": 3})
+
+    def test_deprecated_name_warning(self):
+        """Test that NormalizeLabelsInDatasetd is properly deprecated.
+
+        The deprecation warning only triggers when MONAI version >= 1.6 (since="1.6").
+        This test verifies:
+        1. The actual NormalizeLabelsInDatasetd class is marked as deprecated in docstring
+        2. The class is a subclass of RemapLabelsToSequentiald
+        3. The deprecation mechanism works correctly (tested via version_val simulation)
+        4. The actual class functions correctly
+        """
+        import warnings
+
+        from monai.utils import deprecated
+
+        # Verify NormalizeLabelsInDatasetd docstring indicates deprecation
+        self.assertIn("deprecated", NormalizeLabelsInDatasetd.__doc__.lower())
+        self.assertIn("RemapLabelsToSequentiald", NormalizeLabelsInDatasetd.__doc__)
+
+        # Verify NormalizeLabelsInDatasetd is a subclass of RemapLabelsToSequentiald
+        self.assertTrue(issubclass(NormalizeLabelsInDatasetd, RemapLabelsToSequentiald))
+
+        # Test the deprecation mechanism using version_val to simulate version 1.6
+        # This verifies the @deprecated decorator behavior that NormalizeLabelsInDatasetd uses
+        @deprecated(
+            since="1.6",
+            removed="1.8",
+            msg_suffix="Use `RemapLabelsToSequentiald` instead.",
+            version_val="1.6",  # Simulate version 1.6 to trigger warning
+        )
+        class DeprecatedNormalizeLabels(RemapLabelsToSequentiald):
+            pass
+
+        data = {"label": np.array([[[0, 1]]])}
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            transform = DeprecatedNormalizeLabels(keys="label", label_names={"spleen": 1, "background": 0})
+            _ = transform(data)
+
+            # Check that a deprecation warning was raised
+            self.assertEqual(len(w), 1)
+            self.assertTrue(issubclass(w[0].category, FutureWarning))
+            self.assertIn("RemapLabelsToSequentiald", str(w[0].message))
+
+        # Verify the actual NormalizeLabelsInDatasetd class works correctly
+        transform_actual = NormalizeLabelsInDatasetd(keys="label", label_names={"spleen": 1, "background": 0})
+        result = transform_actual({"label": np.array([[[0, 1]]])})
+        self.assertIn("label", result)
+
 
 class TestResizeGuidanceMultipleLabelCustomd(unittest.TestCase):