Added (Rand)ScaleScaleIntensityFixedMean(d) and modified (Rand)Adjust…

…Contrast(d) (#6542)

### Description

This PR adds the intensity transform **ScaleIntensityFixedMean**
(including its random and random dictionary version) and modifies the
intensity transform **AdjustContrast** and its dictionary version. It
adds functionality available in the corresponding nnU-Net transforms
[**ContrastAugmentationTransform**](https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/transforms/color_transforms.py#L25)
and
[**GammaTransform**](https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/transforms/color_transforms.py#L132).

Specifically, **ScaleIntensityFixedMean** scales the intensity of the
input image by a factor _v = v * (1 + factor)_ (same as the existing
**ScaleIntensity** transform when used with the factor argument). The
added functionality is provided by two arguments:

1. _fixed_mean_: subtract the mean intensity before scaling with
_factor_, then add the same value after scaling to ensure that the
output has the same mean intensity as the input.
  
2. _preserve_range_: clips the output array/tensor to the range of the
input array/tensor
  
AdjustContrast was modified by adding two arguments:

1. _invert_image_: multiplies all intensity values by -1 before gamma
transform and again after gamma transform
  
2. _retain_stats_: applies a scaling factor and an offset to all
intensity values after the gamma transform to ensure that the output
intensity distribution has the same mean and standard deviation as the
intensity distribution of the input


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [x] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Aaron Kujawa <askujawa@gmail.com>

docs/source/transforms.rst

@@ -317,6 +317,18 @@ Intensity
     :members:
     :special-members: __call__
 
+`ScaleIntensityFixedMean`
+"""""""""""""""""""""""""
+.. autoclass:: ScaleIntensityFixedMean
+    :members:
+    :special-members: __call__
+
+`RandScaleIntensityFixedMean`
+"""""""""""""""""""""""""""""
+.. autoclass:: RandScaleIntensityFixedMean
+    :members:
+    :special-members: __call__
+
 `NormalizeIntensity`
 """"""""""""""""""""
 .. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/NormalizeIntensity.png
@@ -1386,6 +1398,12 @@ Intensity (Dict)
     :members:
     :special-members: __call__
 
+`RandScaleIntensityFixedMeand`
+"""""""""""""""""""""""""""""""
+.. autoclass:: RandScaleIntensityFixedMeand
+    :members:
+    :special-members: __call__
+
 `NormalizeIntensityd`
 """""""""""""""""""""
 .. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/NormalizeIntensityd.png

monai/transforms/__init__.py

@@ -118,10 +118,12 @@
     RandKSpaceSpikeNoise,
     RandRicianNoise,
     RandScaleIntensity,
+    RandScaleIntensityFixedMean,
     RandShiftIntensity,
     RandStdShiftIntensity,
     SavitzkyGolaySmooth,
     ScaleIntensity,
+    ScaleIntensityFixedMean,
     ScaleIntensityRange,
     ScaleIntensityRangePercentiles,
     ShiftIntensity,
@@ -198,6 +200,9 @@
     RandScaleIntensityd,
     RandScaleIntensityD,
     RandScaleIntensityDict,
+    RandScaleIntensityFixedMeand,
+    RandScaleIntensityFixedMeanD,
+    RandScaleIntensityFixedMeanDict,
     RandShiftIntensityd,
     RandShiftIntensityD,
     RandShiftIntensityDict,

monai/transforms/intensity/array.py

@@ -48,6 +48,8 @@
     "RandBiasField",
     "ScaleIntensity",
     "RandScaleIntensity",
+    "ScaleIntensityFixedMean",
+    "RandScaleIntensityFixedMean",
     "NormalizeIntensity",
     "ThresholdIntensity",
     "ScaleIntensityRange",
@@ -466,6 +468,161 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         return ret
 
 
+class ScaleIntensityFixedMean(Transform):
+    """
+    Scale the intensity of input image by ``v = v * (1 + factor)``, then shift the output so that the output image has the
+    same mean as the input.
+    """
+
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    def __init__(
+        self,
+        factor: float = 0,
+        preserve_range: bool = False,
+        fixed_mean: bool = True,
+        channel_wise: bool = False,
+        dtype: DtypeLike = np.float32,
+    ) -> None:
+        """
+        Args:
+            factor: factor scale by ``v = v * (1 + factor)``.
+            preserve_range: clips the output array/tensor to the range of the input array/tensor
+            fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
+                to ensure that the output has the same mean as the input.
+            channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
+            on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
+            channel of the image if True.
+            dtype: output data type, if None, same as input image. defaults to float32.
+        """
+        self.factor = factor
+        self.preserve_range = preserve_range
+        self.fixed_mean = fixed_mean
+        self.channel_wise = channel_wise
+        self.dtype = dtype
+
+    def __call__(self, img: NdarrayOrTensor, factor=None) -> NdarrayOrTensor:
+        """
+        Apply the transform to `img`.
+        Args:
+            img: the input tensor/array
+            factor: factor scale by ``v = v * (1 + factor)``
+
+        """
+
+        factor = factor if factor is not None else self.factor
+
+        img = convert_to_tensor(img, track_meta=get_track_meta())
+        img_t = convert_to_tensor(img, track_meta=False)
+        ret: NdarrayOrTensor
+        if self.channel_wise:
+            out = []
+            for d in img_t:
+                if self.preserve_range:
+                    clip_min = d.min()
+                    clip_max = d.max()
+
+                if self.fixed_mean:
+                    mn = d.mean()
+                    d = d - mn
+
+                out_channel = d * (1 + factor)
+
+                if self.fixed_mean:
+                    out_channel = out_channel + mn
+
+                if self.preserve_range:
+                    out_channel = clip(out_channel, clip_min, clip_max)
+
+                out.append(out_channel)
+            ret = torch.stack(out)  # type: ignore
+        else:
+            if self.preserve_range:
+                clip_min = img_t.min()
+                clip_max = img_t.max()
+
+            if self.fixed_mean:
+                mn = img_t.mean()
+                img_t = img_t - mn
+
+            ret = img_t * (1 + factor)
+
+            if self.fixed_mean:
+                ret = ret + mn
+
+            if self.preserve_range:
+                ret = clip(ret, clip_min, clip_max)
+
+        ret = convert_to_dst_type(ret, dst=img, dtype=self.dtype or img_t.dtype)[0]
+        return ret
+
+
+class RandScaleIntensityFixedMean(RandomizableTransform):
+    """
+    Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
+    is randomly picked. Subtract the mean intensity before scaling with `factor`, then add the same value after scaling
+    to ensure that the output has the same mean as the input.
+    """
+
+    backend = ScaleIntensityFixedMean.backend
+
+    def __init__(
+        self,
+        prob: float = 0.1,
+        factors: Sequence[float] | float = 0,
+        fixed_mean: bool = True,
+        preserve_range: bool = False,
+        dtype: DtypeLike = np.float32,
+    ) -> None:
+        """
+        Args:
+            factors: factor range to randomly scale by ``v = v * (1 + factor)``.
+                if single number, factor value is picked from (-factors, factors).
+            preserve_range: clips the output array/tensor to the range of the input array/tensor
+            fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
+                to ensure that the output has the same mean as the input.
+            channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
+            on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
+            channel of the image if True.
+            dtype: output data type, if None, same as input image. defaults to float32.
+
+        """
+        RandomizableTransform.__init__(self, prob)
+        if isinstance(factors, (int, float)):
+            self.factors = (min(-factors, factors), max(-factors, factors))
+        elif len(factors) != 2:
+            raise ValueError("factors should be a number or pair of numbers.")
+        else:
+            self.factors = (min(factors), max(factors))
+        self.factor = self.factors[0]
+        self.fixed_mean = fixed_mean
+        self.preserve_range = preserve_range
+        self.dtype = dtype
+
+        self.scaler = ScaleIntensityFixedMean(
+            factor=self.factor, fixed_mean=self.fixed_mean, preserve_range=self.preserve_range, dtype=self.dtype
+        )
+
+    def randomize(self, data: Any | None = None) -> None:
+        super().randomize(None)
+        if not self._do_transform:
+            return None
+        self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])
+
+    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
+        """
+        Apply the transform to `img`.
+        """
+        img = convert_to_tensor(img, track_meta=get_track_meta())
+        if randomize:
+            self.randomize()
+
+        if not self._do_transform:
+            return convert_data_type(img, dtype=self.dtype)[0]
+
+        return self.scaler(img, self.factor)
+
+
 class RandScaleIntensity(RandomizableTransform):
     """
     Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
@@ -799,48 +956,99 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
 
 class AdjustContrast(Transform):
     """
-    Changes image intensity by gamma. Each pixel/voxel intensity is updated as::
+    Changes image intensity with gamma transform. Each pixel/voxel intensity is updated as::
 
         x = ((x - min) / intensity_range) ^ gamma * intensity_range + min
 
     Args:
         gamma: gamma value to adjust the contrast as function.
+        invert_image: whether to invert the image before applying gamma augmentation. If True, multiply all intensity
+            values with -1 before the gamma transform and again after the gamma transform. This behaviour is mimicked
+            from `nnU-Net <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
+            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
+            function.
+        retain_stats: if True, applies a scaling factor and an offset to all intensity values after gamma transform to
+            ensure that the output intensity distribution has the same mean and standard deviation as the intensity
+            distribution of the input. This behaviour is mimicked from `nnU-Net
+            <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
+            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
+            function.
     """
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
-    def __init__(self, gamma: float) -> None:
+    def __init__(self, gamma: float, invert_image: bool = False, retain_stats: bool = False) -> None:
         if not isinstance(gamma, (int, float)):
             raise ValueError(f"gamma must be a float or int number, got {type(gamma)} {gamma}.")
         self.gamma = gamma
+        self.invert_image = invert_image
+        self.retain_stats = retain_stats
 
-    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
+    def __call__(self, img: NdarrayOrTensor, gamma=None) -> NdarrayOrTensor:
         """
         Apply the transform to `img`.
+        gamma: gamma value to adjust the contrast as function.
         """
         img = convert_to_tensor(img, track_meta=get_track_meta())
+        gamma = gamma if gamma is not None else self.gamma
+
+        if self.invert_image:
+            img = -img
+
+        if self.retain_stats:
+            mn = img.mean()
+            sd = img.std()
+
         epsilon = 1e-7
         img_min = img.min()
         img_range = img.max() - img_min
-        ret: NdarrayOrTensor = ((img - img_min) / float(img_range + epsilon)) ** self.gamma * img_range + img_min
+        ret: NdarrayOrTensor = ((img - img_min) / float(img_range + epsilon)) ** gamma * img_range + img_min
+
+        if self.retain_stats:
+            # zero mean and normalize
+            ret = ret - ret.mean()
+            ret = ret / (ret.std() + 1e-8)
+            # restore old mean and standard deviation
+            ret = sd * ret + mn
+
+        if self.invert_image:
+            ret = -ret
+
         return ret
 
 
 class RandAdjustContrast(RandomizableTransform):
     """
-    Randomly changes image intensity by gamma. Each pixel/voxel intensity is updated as::
+    Randomly changes image intensity with gamma transform. Each pixel/voxel intensity is updated as:
 
         x = ((x - min) / intensity_range) ^ gamma * intensity_range + min
 
     Args:
         prob: Probability of adjustment.
         gamma: Range of gamma values.
             If single number, value is picked from (0.5, gamma), default is (0.5, 4.5).
+        invert_image: whether to invert the image before applying gamma augmentation. If True, multiply all intensity
+            values with -1 before the gamma transform and again after the gamma transform. This behaviour is mimicked
+            from `nnU-Net <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
+            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
+            function.
+        retain_stats: if True, applies a scaling factor and an offset to all intensity values after gamma transform to
+            ensure that the output intensity distribution has the same mean and standard deviation as the intensity
+            distribution of the input. This behaviour is mimicked from `nnU-Net
+            <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
+            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
+            function.
     """
 
     backend = AdjustContrast.backend
 
-    def __init__(self, prob: float = 0.1, gamma: Sequence[float] | float = (0.5, 4.5)) -> None:
+    def __init__(
+        self,
+        prob: float = 0.1,
+        gamma: Sequence[float] | float = (0.5, 4.5),
+        invert_image: bool = False,
+        retain_stats: bool = False,
+    ) -> None:
         RandomizableTransform.__init__(self, prob)
 
         if isinstance(gamma, (int, float)):
@@ -854,7 +1062,13 @@ def __init__(self, prob: float = 0.1, gamma: Sequence[float] | float = (0.5, 4.5
         else:
             self.gamma = (min(gamma), max(gamma))
 
-        self.gamma_value: float | None = None
+        self.gamma_value: float = 1.0
+        self.invert_image: bool = invert_image
+        self.retain_stats: bool = retain_stats
+
+        self.adjust_contrast = AdjustContrast(
+            self.gamma_value, invert_image=self.invert_image, retain_stats=self.retain_stats
+        )
 
     def randomize(self, data: Any | None = None) -> None:
         super().randomize(None)
@@ -875,7 +1089,8 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
 
         if self.gamma_value is None:
             raise RuntimeError("gamma_value is not set, please call `randomize` function first.")
-        return AdjustContrast(self.gamma_value)(img)
+
+        return self.adjust_contrast(img, self.gamma_value)
 
 
 class ScaleIntensityRangePercentiles(Transform):