Repository: Project-MONAI/MONAI

Issue:
Add channel_wise option to RandShiftIntensity and RandScaleIntensity
It is very common practice to independently augment intensities of separate modalities. Yet currently RandShiftIntensity and RandScaleIntensity do not have this option, and instead randomly shift/scale along all channels at once.


It is noteworthy that RandStdShiftIntensity has a "channel_wise" option to do just this. I suggest we add a similar option to RandShiftIntensity and RandScaleIntensity.
Add channel_wise option to RandShiftIntensity and RandScaleIntensity
It is very common practice to independently augment intensities of separate modalities. Yet currently RandShiftIntensity and RandScaleIntensity do not have this option, and instead randomly shift/scale along all channels at once.


It is noteworthy that RandStdShiftIntensity has a "channel_wise" option to do just this. I suggest we add a similar option to RandShiftIntensity and RandScaleIntensity.


Discussion:
Hi @philnovv, I think for `RandScaleIntensity` and `RandShiftIntensity` since we will randomly choose a `factor` or `offset` then no matter do it channel_wise or not, the result will be the same. Or do you mean randomly select a `factor` or `offset` for each channel?
If I have misunderstood, just correct me.
Thanks!
Hi @KumoLiu, the latter is what I mean. The option to randomly select and apply a factor/offset for each channel would be (I think) more closely aligned with standard practice.
BTW this applies to all intensity-based augmentation transforms. For example we may want to apply random blurring with an independent blurring kernel for each channel. This makes sense as an augmentation strategy because the different contrasts in real-world multi-contrast images are often acquired with very different resolutions/slice thicknesses etc...
Hi @philnovv, I think for `RandScaleIntensity` and `RandShiftIntensity` since we will randomly choose a `factor` or `offset` then no matter do it channel_wise or not, the result will be the same. Or do you mean randomly select a `factor` or `offset` for each channel?
If I have misunderstood, just correct me.
Thanks!
Hi @KumoLiu, the latter is what I mean. The option to randomly select and apply a factor/offset for each channel would be (I think) more closely aligned with standard practice.
BTW this applies to all intensity-based augmentation transforms. For example we may want to apply random blurring with an independent blurring kernel for each channel. This makes sense as an augmentation strategy because the different contrasts in real-world multi-contrast images are often acquired with very different resolutions/slice thicknesses etc...

Resolving patch:
diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -255,7 +255,9 @@ class RandShiftIntensity(RandomizableTransform):
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
-    def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1) -> None:
+    def __init__(
+        self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1, channel_wise: bool = False
+    ) -> None:
         """
         Args:
             offsets: offset range to randomly shift.
@@ -263,6 +265,8 @@ def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, pro
             safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
                 E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
             prob: probability of shift.
+            channel_wise: if True, shift intensity on each channel separately. For each channel, a random offset will be chosen.
+                Please ensure that the first dimension represents the channel of the image if True.
         """
         RandomizableTransform.__init__(self, prob)
         if isinstance(offsets, (int, float)):
@@ -272,13 +276,17 @@ def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, pro
         else:
             self.offsets = (min(offsets), max(offsets))
         self._offset = self.offsets[0]
+        self.channel_wise = channel_wise
         self._shifter = ShiftIntensity(self._offset, safe)
 
     def randomize(self, data: Any | None = None) -> None:
         super().randomize(None)
         if not self._do_transform:
             return None
-        self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
+        if self.channel_wise:
+            self._offset = [self.R.uniform(low=self.offsets[0], high=self.offsets[1]) for _ in range(data.shape[0])]  # type: ignore
+        else:
+            self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
 
     def __call__(self, img: NdarrayOrTensor, factor: float | None = None, randomize: bool = True) -> NdarrayOrTensor:
         """
@@ -292,12 +300,21 @@ def __call__(self, img: NdarrayOrTensor, factor: float | None = None, randomize:
         """
         img = convert_to_tensor(img, track_meta=get_track_meta())
         if randomize:
-            self.randomize()
+            self.randomize(img)
 
         if not self._do_transform:
             return img
 
-        return self._shifter(img, self._offset if factor is None else self._offset * factor)
+        ret: NdarrayOrTensor
+        if self.channel_wise:
+            out = []
+            for i, d in enumerate(img):
+                out_channel = self._shifter(d, self._offset[i] if factor is None else self._offset[i] * factor)  # type: ignore
+                out.append(out_channel)
+            ret = torch.stack(out)  # type: ignore
+        else:
+            ret = self._shifter(img, self._offset if factor is None else self._offset * factor)
+        return ret
 
 
 class StdShiftIntensity(Transform):
diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py
--- a/monai/transforms/intensity/dictionary.py
+++ b/monai/transforms/intensity/dictionary.py
@@ -373,6 +373,7 @@ def __init__(
         meta_keys: KeysCollection | None = None,
         meta_key_postfix: str = DEFAULT_POST_FIX,
         prob: float = 0.1,
+        channel_wise: bool = False,
         allow_missing_keys: bool = False,
     ) -> None:
         """
@@ -399,6 +400,8 @@ def __init__(
                 used to extract the factor value is `factor_key` is not None.
             prob: probability of shift.
                 (Default 0.1, with 10% probability it returns an array shifted intensity.)
+            channel_wise: if True, shift intensity on each channel separately. For each channel, a random offset will be chosen.
+                Please ensure that the first dimension represents the channel of the image if True.
             allow_missing_keys: don't raise exception if key is missing.
         """
         MapTransform.__init__(self, keys, allow_missing_keys)
@@ -409,7 +412,7 @@ def __init__(
         if len(self.keys) != len(self.meta_keys):
             raise ValueError("meta_keys should have the same length as keys.")
         self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))
-        self.shifter = RandShiftIntensity(offsets=offsets, safe=safe, prob=1.0)
+        self.shifter = RandShiftIntensity(offsets=offsets, safe=safe, prob=1.0, channel_wise=channel_wise)
 
     def set_random_state(
         self, seed: int | None = None, state: np.random.RandomState | None = None
@@ -426,8 +429,15 @@ def __call__(self, data) -> dict[Hashable, NdarrayOrTensor]:
                 d[key] = convert_to_tensor(d[key], track_meta=get_track_meta())
             return d
 
+        # expect all the specified keys have same spatial shape and share same random holes
+        first_key: Hashable = self.first_key(d)
+        if first_key == ():
+            for key in self.key_iterator(d):
+                d[key] = convert_to_tensor(d[key], track_meta=get_track_meta())
+            return d
+
         # all the keys share the same random shift factor
-        self.shifter.randomize(None)
+        self.shifter.randomize(d[first_key])
         for key, factor_key, meta_key, meta_key_postfix in self.key_iterator(
             d, self.factor_key, self.meta_keys, self.meta_key_postfix
         ):


Test patch:
diff --git a/tests/test_rand_shift_intensity.py b/tests/test_rand_shift_intensity.py
--- a/tests/test_rand_shift_intensity.py
+++ b/tests/test_rand_shift_intensity.py
@@ -33,6 +33,20 @@ def test_value(self, p):
         expected = self.imt + np.random.uniform(low=-1.0, high=1.0)
         assert_allclose(result, expected, type_test="tensor")
 
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_channel_wise(self, p):
+        scaler = RandShiftIntensity(offsets=3.0, channel_wise=True, prob=1.0)
+        scaler.set_random_state(seed=0)
+        im = p(self.imt)
+        result = scaler(im)
+        np.random.seed(0)
+        # simulate the randomize() of transform
+        np.random.random()
+        channel_num = self.imt.shape[0]
+        factor = [np.random.uniform(low=-3.0, high=3.0) for _ in range(channel_num)]
+        expected = p(np.stack([np.asarray((self.imt[i]) + factor[i]) for i in range(channel_num)]).astype(np.float32))
+        assert_allclose(result, expected, atol=0, rtol=1e-5, type_test=False)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_rand_shift_intensityd.py b/tests/test_rand_shift_intensityd.py
--- a/tests/test_rand_shift_intensityd.py
+++ b/tests/test_rand_shift_intensityd.py
@@ -46,6 +46,22 @@ def test_factor(self):
         expected = self.imt + np.random.uniform(low=-1.0, high=1.0) * np.nanmax(self.imt)
         np.testing.assert_allclose(result[key], expected)
 
+    def test_channel_wise(self):
+        key = "img"
+        for p in TEST_NDARRAYS:
+            scaler = RandShiftIntensityd(keys=[key], offsets=3.0, prob=1.0, channel_wise=True)
+            scaler.set_random_state(seed=0)
+            result = scaler({key: p(self.imt)})
+            np.random.seed(0)
+            # simulate the randomize function of transform
+            np.random.random()
+            channel_num = self.imt.shape[0]
+            factor = [np.random.uniform(low=-3.0, high=3.0) for _ in range(channel_num)]
+            expected = p(
+                np.stack([np.asarray((self.imt[i]) + factor[i]) for i in range(channel_num)]).astype(np.float32)
+            )
+            assert_allclose(result[key], p(expected), type_test="tensor")
+
 
 if __name__ == "__main__":
     unittest.main()