Enable global coordinates in spatial crop transforms

monai/transforms/__init__.py

@@ -676,6 +676,12 @@
     ToTensord,
     ToTensorD,
     ToTensorDict,
+    TransformPointsImageToWorldd,
+    TransformPointsImageToWorldD,
+    TransformPointsImageToWorldDict,
+    TransformPointsWorldToImaged,
+    TransformPointsWorldToImageD,
+    TransformPointsWorldToImageDict,
     Transposed,
     TransposeD,
     TransposeDict,

monai/transforms/croppad/dictionary.py

@@ -50,7 +50,7 @@
 from monai.transforms.traits import LazyTrait, MultiSampleTrait
 from monai.transforms.transform import LazyTransform, MapTransform, Randomizable
 from monai.transforms.utils import is_positive
-from monai.utils import MAX_SEED, Method, PytorchPadMode, ensure_tuple_rep
+from monai.utils import MAX_SEED, Method, PytorchPadMode, TraceKeys, ensure_tuple_rep
 
 __all__ = [
     "Padd",
@@ -431,17 +431,33 @@ class SpatialCropd(Cropd):
         - a spatial center and size
         - the start and end coordinates of the ROI
 
+    ROI parameters (``roi_center``, ``roi_size``, ``roi_start``, ``roi_end``) can also be specified as
+    string dictionary keys. When a string is provided, the actual coordinate values are read from the
+    data dictionary at call time. This enables pipelines where coordinates are computed by earlier
+    transforms (e.g., :py:class:`monai.transforms.TransformPointsWorldToImaged`) and stored in the
+    data dictionary under the given key.
+
+    Example::
+
+        from monai.transforms import Compose, TransformPointsWorldToImaged, SpatialCropd
+
+        pipeline = Compose([
+            TransformPointsWorldToImaged(keys="roi_start", refer_keys="image"),
+            TransformPointsWorldToImaged(keys="roi_end", refer_keys="image"),
+            SpatialCropd(keys="image", roi_start="roi_start", roi_end="roi_end"),
+        ])
+
     This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
     for more information.
     """
 
     def __init__(
         self,
         keys: KeysCollection,
-        roi_center: Sequence[int] | int | None = None,
-        roi_size: Sequence[int] | int | None = None,
-        roi_start: Sequence[int] | int | None = None,
-        roi_end: Sequence[int] | int | None = None,
+        roi_center: Sequence[int] | int | str | None = None,
+        roi_size: Sequence[int] | int | str | None = None,
+        roi_start: Sequence[int] | int | str | None = None,
+        roi_end: Sequence[int] | int | str | None = None,
         roi_slices: Sequence[slice] | None = None,
         allow_missing_keys: bool = False,
         lazy: bool = False,
@@ -450,19 +466,126 @@ def __init__(
         Args:
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
-            roi_center: voxel coordinates for center of the crop ROI.
+            roi_center: voxel coordinates for center of the crop ROI, or a string key to look up
+                the coordinates from the data dictionary.
             roi_size: size of the crop ROI, if a dimension of ROI size is larger than image size,
-                will not crop that dimension of the image.
-            roi_start: voxel coordinates for start of the crop ROI.
+                will not crop that dimension of the image. Can also be a string key.
+            roi_start: voxel coordinates for start of the crop ROI, or a string key to look up
+                the coordinates from the data dictionary.
             roi_end: voxel coordinates for end of the crop ROI, if a coordinate is out of image,
-                use the end coordinate of image.
+                use the end coordinate of image. Can also be a string key.
             roi_slices: list of slices for each of the spatial dimensions.
             allow_missing_keys: don't raise exception if key is missing.
             lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.
         """
-        cropper = SpatialCrop(roi_center, roi_size, roi_start, roi_end, roi_slices, lazy=lazy)
+        self._roi_center = roi_center
+        self._roi_size = roi_size
+        self._roi_start = roi_start
+        self._roi_end = roi_end
+        self._roi_slices = roi_slices
+        self._has_str_roi = any(isinstance(v, str) for v in [roi_center, roi_size, roi_start, roi_end])
+
+        if not self._has_str_roi:
+            cropper = SpatialCrop(roi_center, roi_size, roi_start, roi_end, roi_slices, lazy=lazy)
+        else:
+            # Placeholder cropper for the string-key path. Replaced on self.cropper at
+            # __call__ time once string keys are resolved from the data dictionary.
+            cropper = SpatialCrop(roi_start=[0], roi_end=[1], lazy=lazy)
         super().__init__(keys, cropper=cropper, allow_missing_keys=allow_missing_keys, lazy=lazy)
 
+    @staticmethod
+    def _resolve_roi_param(val, d):
+        """Resolve an ROI parameter from the data dictionary if it is a string key.
+
+        Args:
+            val: the ROI parameter value. If a string, it is used as a key to look up
+                the actual value from ``d``. Otherwise returned as-is.
+            d: the data dictionary.
+
+        Returns:
+            The resolved ROI parameter. Tensors and numpy arrays are flattened to 1-D
+            and rounded to int64 so they can be consumed by ``Crop.compute_slices``.
+
+        Raises:
+            KeyError: if ``val`` is a string key that does not exist in ``d``.
+        """
+        if not isinstance(val, str):
+            return val
+        if val not in d:
+            raise KeyError(f"ROI key '{val}' not found in the data dictionary.")
+        resolved = d[val]
+        # ApplyTransformToPoints outputs tensors of shape (C, N, dims).
+        # A single coordinate like [142.5, -67.3, 301.8] becomes shape (1, 1, 3).
+        # Flatten to 1-D and round to integers for compute_slices.
+        # Uses banker's rounding (torch.round) to avoid systematic bias in spatial coordinates.
+        if isinstance(resolved, np.ndarray):
+            resolved = torch.from_numpy(resolved)
+        if isinstance(resolved, torch.Tensor):
+            resolved = torch.round(resolved.flatten()).to(torch.int64)
+        return resolved
+
+    @property
+    def requires_current_data(self):
+        """bool: Whether this transform requires the current data dictionary to resolve ROI parameters."""
+        return self._has_str_roi
+
+    def __call__(self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = None) -> dict[Hashable, torch.Tensor]:
+        """
+        Args:
+            data: dictionary of data items to be transformed.
+            lazy: whether to execute lazily. If ``None``, uses the instance default.
+
+        Returns:
+            Dictionary with cropped data for each key.
+        """
+        if not self._has_str_roi:
+            return super().__call__(data, lazy=lazy)
+
+        d = dict(data)
+        roi_center = self._resolve_roi_param(self._roi_center, d)
+        roi_size = self._resolve_roi_param(self._roi_size, d)
+        roi_start = self._resolve_roi_param(self._roi_start, d)
+        roi_end = self._resolve_roi_param(self._roi_end, d)
+
+        lazy_ = self.lazy if lazy is None else lazy
+        self.cropper = SpatialCrop(
+            roi_center=roi_center,
+            roi_size=roi_size,
+            roi_start=roi_start,
+            roi_end=roi_end,
+            roi_slices=self._roi_slices,
+            lazy=lazy_,
+        )
+        for key in self.key_iterator(d):
+            d[key] = self.cropper(d[key], lazy=lazy_)
+        return d
+
+    def inverse(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTensor]:
+        """
+        Inverse of the crop transform, restoring the original spatial dimensions via padding.
+
+        For the string-key path, ``self.cropper`` is recreated on each ``__call__``, so its
+        ``id()`` won't match the one stored in the MetaTensor's transform stack. This override
+        bypasses the ID check and applies the inverse directly using the crop info stored in the
+        MetaTensor.
+
+        Args:
+            data: dictionary of cropped ``MetaTensor`` items.
+
+        Returns:
+            Dictionary with inverse-transformed (padded) data for each key.
+        """
+        if not self._has_str_roi:
+            return super().inverse(data)
+        d = dict(data)
+        for key in self.key_iterator(d):
+            transform = self.cropper.pop_transform(d[key], check=False)
+            cropped = transform[TraceKeys.EXTRA_INFO]["cropped"]
+            inverse_transform = BorderPad(cropped)
+            with inverse_transform.trace_transform(False):
+                d[key] = inverse_transform(d[key])
+        return d
+
 
 class CenterSpatialCropd(Cropd):
     """

monai/transforms/utility/dictionary.py

@@ -192,6 +192,12 @@
     "ApplyTransformToPointsd",
     "ApplyTransformToPointsD",
     "ApplyTransformToPointsDict",
+    "TransformPointsWorldToImaged",
+    "TransformPointsWorldToImageD",
+    "TransformPointsWorldToImageDict",
+    "TransformPointsImageToWorldd",
+    "TransformPointsImageToWorldD",
+    "TransformPointsImageToWorldDict",
     "FlattenSequenced",
     "FlattenSequenceD",
     "FlattenSequenceDict",
@@ -1910,6 +1916,86 @@ def inverse(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch
         return d
 
 
+class TransformPointsWorldToImaged(ApplyTransformToPointsd):
+    """
+    Dictionary-based transform to convert points from world coordinates to image coordinates.
+
+    This is a convenience subclass of :py:class:`monai.transforms.ApplyTransformToPointsd` with
+    ``invert_affine=True``, which transforms world-space coordinates into the coordinate space of a
+    reference image by inverting the image's affine matrix.
+
+    Args:
+        keys: keys of the corresponding items to be transformed.
+            See also: monai.transforms.MapTransform
+        refer_keys: The key of the reference image used to derive the affine transformation.
+            This is required because the affine must come from a reference image.
+            It can also be a sequence of keys, in which case each refers to the affine applied
+            to the matching points in ``keys``.
+        dtype: The desired data type for the output.
+        affine_lps_to_ras: Defaults to ``False``. Set to ``True`` if your point data is in the RAS
+            coordinate system or you're using ``ITKReader`` with ``affine_lps_to_ras=True``.
+        allow_missing_keys: Don't raise exception if key is missing.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        refer_keys: KeysCollection,
+        dtype: DtypeLike | torch.dtype = torch.float64,
+        affine_lps_to_ras: bool = False,
+        allow_missing_keys: bool = False,
+    ):
+        super().__init__(
+            keys=keys,
+            refer_keys=refer_keys,
+            dtype=dtype,
+            affine=None,
+            invert_affine=True,
+            affine_lps_to_ras=affine_lps_to_ras,
+            allow_missing_keys=allow_missing_keys,
+        )
+
+
+class TransformPointsImageToWorldd(ApplyTransformToPointsd):
+    """
+    Dictionary-based transform to convert points from image coordinates to world coordinates.
+
+    This is a convenience subclass of :py:class:`monai.transforms.ApplyTransformToPointsd` with
+    ``invert_affine=False``, which transforms image-space coordinates into world-space coordinates
+    by applying the reference image's affine matrix directly.
+
+    Args:
+        keys: keys of the corresponding items to be transformed.
+            See also: monai.transforms.MapTransform
+        refer_keys: The key of the reference image used to derive the affine transformation.
+            This is required because the affine must come from a reference image.
+            It can also be a sequence of keys, in which case each refers to the affine applied
+            to the matching points in ``keys``.
+        dtype: The desired data type for the output.
+        affine_lps_to_ras: Defaults to ``False``. Set to ``True`` if your point data is in the RAS
+            coordinate system or you're using ``ITKReader`` with ``affine_lps_to_ras=True``.
+        allow_missing_keys: Don't raise exception if key is missing.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        refer_keys: KeysCollection,
+        dtype: DtypeLike | torch.dtype = torch.float64,
+        affine_lps_to_ras: bool = False,
+        allow_missing_keys: bool = False,
+    ):
+        super().__init__(
+            keys=keys,
+            refer_keys=refer_keys,
+            dtype=dtype,
+            affine=None,
+            invert_affine=False,
+            affine_lps_to_ras=affine_lps_to_ras,
+            allow_missing_keys=allow_missing_keys,
+        )
+
+
 class FlattenSequenced(MapTransform, ReduceTrait):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.FlattenSequence`.
@@ -1975,4 +2061,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
 AddCoordinateChannelsD = AddCoordinateChannelsDict = AddCoordinateChannelsd
 FlattenSubKeysD = FlattenSubKeysDict = FlattenSubKeysd
 ApplyTransformToPointsD = ApplyTransformToPointsDict = ApplyTransformToPointsd
+TransformPointsWorldToImageD = TransformPointsWorldToImageDict = TransformPointsWorldToImaged
+TransformPointsImageToWorldD = TransformPointsImageToWorldDict = TransformPointsImageToWorldd
 FlattenSequenceD = FlattenSequenceDict = FlattenSequenced