diff --git a/torch_em/transform/nnunet_raw.py b/torch_em/transform/nnunet_raw.py
new file mode 100644
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+++ b/torch_em/transform/nnunet_raw.py
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+import json
+import numpy as np
+
+
+class nnUNetRawTransform:
+    """Apply transformation on the raw inputs.
+    Adapted from nnUNetv2's `ImageNormalization`:
+        - https://github.com/MIC-DKFZ/nnUNet/tree/master/nnunetv2/preprocessing/normalization
+
+    You can use this class to apply the necessary raw transformations on input modalities.
+
+    (Current Support - CT and PET): The inputs should be of dimension 2 * (H * W * D).
+        - The first channel should be CT volume
+        - The second channel should be PET volume
+
+    Here's an example for how to use this class:
+    ```python
+    # Initialize the raw transform.
+    raw_transform = nnUNetRawTransform(plans_file=".../nnUNetPlans.json")
+
+    # Apply transformation on the inputs.
+    patient_vol = np.concatenate(ct_vol, pet_vol)
+    patient_transformed = raw_transform(patient_vol)
+    ```
+    """
+    def __init__(
+            self,
+            plans_file: str,
+            expected_dtype: type = np.float32,
+            tolerance: float = 1e-8,
+            model_name: str = "3d_fullres"
+    ):
+        self.expected_dtype = expected_dtype
+        self.tolerance = tolerance
+
+        json_file = self.load_json(plans_file)
+        self.intensity_properties = json_file["foreground_intensity_properties_per_channel"]
+        self.per_channel_scheme = json_file["configurations"][model_name]["normalization_schemes"]
+
+    def load_json(self, _file: str):
+        # source: `batchgenerators.utilities.file_and_folder_operations`
+        with open(_file, 'r') as f:
+            a = json.load(f)
+        return a
+
+    def ct_transform(self, channel, properties):
+        mean = properties['mean']
+        std = properties['std']
+        lower_bound = properties['percentile_00_5']
+        upper_bound = properties['percentile_99_5']
+
+        transformed_channel = np.clip(channel, lower_bound, upper_bound)
+        transformed_channel = (transformed_channel - mean) / max(std, self.tolerance)
+        return transformed_channel
+
+    def __call__(
+            self,
+            raw: np.ndarray
+    ) -> np.ndarray:  # the transformed raw inputs
+        """Returns the raw inputs after applying the pre-processing from nnUNet.
+
+        Args:
+            raw: The raw array inputs
+                Expectd a float array of shape M * (H * W * D) (where, M is the number of modalities)
+        Returns:
+            The transformed raw inputs (the same shape as inputs)
+        """
+        assert raw.shape[0] == len(self.per_channel_scheme), "Number of channels & transforms from data plan must match"
+
+        raw = raw.astype(self.expected_dtype)
+
+        normalized_channels = []
+        for idxx, (channel_transform, channel) in enumerate(zip(self.per_channel_scheme, raw)):
+            properties = self.intensity_properties[str(idxx)]
+
+            # get the correct transformation function, this can for example be a method of this class
+            if channel_transform == "CTNormalization":
+                channel = self.ct_transform(channel, properties)
+            elif channel_transform in [
+                "ZScoreNormalization", "NoNormalization", "RescaleTo01Normalization", "RGBTo01Normalization"
+            ]:
+                raise NotImplementedError(f"{channel_transform} is not supported by nnUNetRawTransform yet.")
+            else:
+                raise ValueError(f"Transform is not known: {channel_transform}.")
+
+            normalized_channels.append(channel)
+
+        return np.stack(normalized_channels)