Add multi-channel conversion transforms for brats23

monai/transforms/__init__.py

@@ -516,6 +516,8 @@
     CastToType,
     ClassesToIndices,
     ConvertToMultiChannelBasedOnBratsClasses,
+    ConvertToMultiChannelBasedOnBrats23Classes,
+    ConvertToMultiChannelBasedOnBrats23ClassesNoReg,
     CuCIM,
     DataStats,
     EnsureChannelFirst,
@@ -569,6 +571,12 @@
     ConvertToMultiChannelBasedOnBratsClassesd,
     ConvertToMultiChannelBasedOnBratsClassesD,
     ConvertToMultiChannelBasedOnBratsClassesDict,
+    ConvertToMultiChannelBasedOnBrats23ClassesD,
+    ConvertToMultiChannelBasedOnBrats23Classesd,
+    ConvertToMultiChannelBasedOnBrats23ClassesDict,
+    ConvertToMultiChannelBasedOnBrats23ClassesNoRegD,
+    ConvertToMultiChannelBasedOnBrats23ClassesNoRegd,
+    ConvertToMultiChannelBasedOnBrats23ClassesNoRegDict,
     CopyItemsd,
     CopyItemsD,
     CopyItemsDict,

monai/transforms/utility/array.py

@@ -97,6 +97,8 @@
     "FgBgToIndices",
     "ClassesToIndices",
     "ConvertToMultiChannelBasedOnBratsClasses",
+    "ConvertToMultiChannelBasedOnBrats23Classes",
+    "ConvertToMultiChannelBasedOnBrats23ClassesNoReg",
     "AddExtremePointsChannel",
     "TorchVision",
     "MapLabelValue",
@@ -1070,7 +1072,49 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
         # label 4 is ET
         return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)
+
 
+class ConvertToMultiChannelBasedOnBrats23Classes(Transform):
+    """
+    Convert labels to multi channels based on brats23 classes:
+    label 1 is the necrotic and non-enhancing tumor core (NCR)
+    label 2 is the peritumoral edema (ED)
+    label 3 is the GD-enhancing tumor (ET)
+    NOTE: REGION-BASED CONVERSION to TC, WT, ET
+    """
+
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    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 == 3), (img == 1) | (img == 3) | (img == 2), img == 3]  # -> tc, wt, et
+        # merge labels 1 (ncr) and 3 (et) and 2 (ed) to WT
+        return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)
+
+
+class ConvertToMultiChannelBasedOnBrats23ClassesNoReg(Transform):
+    """
+    Convert labels to multi channels based on brats23 classes:
+    label 1 is the necrotic and non-enhancing tumor core (NCR)
+    label 2 is the peritumoral edema (ED)
+    label 3 is the GD-enhancing tumor (ET)
+    NOTE: LABEL-BASED CONVERSION
+    """
+
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    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 == 2), (img == 3)]
+
+        return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)
+
 
 class AddExtremePointsChannel(Randomizable, Transform):
     """

monai/transforms/utility/dictionary.py

@@ -40,6 +40,8 @@
     CastToType,
     ClassesToIndices,
     ConvertToMultiChannelBasedOnBratsClasses,
+    ConvertToMultiChannelBasedOnBrats23Classes,
+    ConvertToMultiChannelBasedOnBrats23ClassesNoReg,
     CuCIM,
     DataStats,
     EnsureChannelFirst,
@@ -89,6 +91,12 @@
     "ConvertToMultiChannelBasedOnBratsClassesD",
     "ConvertToMultiChannelBasedOnBratsClassesDict",
     "ConvertToMultiChannelBasedOnBratsClassesd",
+    "ConvertToMultiChannelBasedOnBrats23ClassesD",
+    "ConvertToMultiChannelBasedOnBrats23ClassesDict",
+    "ConvertToMultiChannelBasedOnBrats23Classesd",
+    "ConvertToMultiChannelBasedOnBrats23NoRegClassesD",
+    "ConvertToMultiChannelBasedOnBrats23NoRegClassesDict",
+    "ConvertToMultiChannelBasedOnBrats23NoRegClassesd",
     "CopyItemsD",
     "CopyItemsDict",
     "CopyItemsd",
@@ -1305,6 +1313,53 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         for key in self.key_iterator(d):
             d[key] = self.converter(d[key])
         return d
+
+
+class ConvertToMultiChannelBasedOnBrats23Classesd(MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.ConvertToMultiChannelBasedOnBrats23Classes`.
+    Convert labels to multi channels based on brats23 classes:
+    label 1 is the necrotic and non-enhancing tumor core
+    label 2 is the peritumoral edema
+    label 3 is the GD-enhancing tumor
+    The possible classes are TC (Tumor core), WT (Whole tumor)
+    and ET (Enhancing tumor).
+    """
+
+    backend = ConvertToMultiChannelBasedOnBrats23Classes.backend
+
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
+        super().__init__(keys, allow_missing_keys)
+        self.converter = ConvertToMultiChannelBasedOnBrats23Classes()
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        for key in self.key_iterator(d):
+            d[key] = self.converter(d[key])
+        return d
+
+
+class ConvertToMultiChannelBasedOnBrats23ClassesNoRegd(MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.ConvertToMultiChannelBasedOnBratsClasses`.
+    Convert labels to multi channels based on brats23 classes:
+    label 1 is the necrotic and non-enhancing tumor core
+    label 2 is the peritumoral edema
+    label 4 is the GD-enhancing tumor
+    In this case, labels are converted to multi channels. No regions are involved.
+    """
+
+    backend = ConvertToMultiChannelBasedOnBrats23ClassesNoReg.backend
+
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False):
+        super().__init__(keys, allow_missing_keys)
+        self.converter = ConvertToMultiChannelBasedOnBrats23ClassesNoReg()
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        for key in self.key_iterator(d):
+            d[key] = self.converter(d[key])
+        return d
 
 
 class AddExtremePointsChanneld(Randomizable, MapTransform):
@@ -1870,6 +1925,12 @@ def inverse(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch
 ConvertToMultiChannelBasedOnBratsClassesD = ConvertToMultiChannelBasedOnBratsClassesDict = (
     ConvertToMultiChannelBasedOnBratsClassesd
 )
+ConvertToMultiChannelBasedOnBrats23ClassesD = ConvertToMultiChannelBasedOnBrats23ClassesDict = (
+    ConvertToMultiChannelBasedOnBrats23Classesd
+)
+ConvertToMultiChannelBasedOnBrats23ClassesNoRegD = ConvertToMultiChannelBasedOnBrats23ClassesNoRegDict = (
+    ConvertToMultiChannelBasedOnBrats23ClassesNoRegd
+)
 AddExtremePointsChannelD = AddExtremePointsChannelDict = AddExtremePointsChanneld
 TorchVisionD = TorchVisionDict = TorchVisiond
 RandTorchVisionD = RandTorchVisionDict = RandTorchVisiond