Make MONAI robust to non-default meta dict names

monai/apps/deepgrow/transforms.py

@@ -19,6 +19,7 @@
 import torch
 
 from monai.config import IndexSelection, KeysCollection, NdarrayOrTensor
+from monai.data.meta_obj import get_meta_dict_name
 from monai.networks.layers import GaussianFilter
 from monai.transforms import Resize, SpatialCrop
 from monai.transforms.transform import MapTransform, Randomizable, Transform
@@ -546,7 +547,12 @@ def _apply(self, pos_clicks, neg_clicks, factor, slice_num):
 
     def __call__(self, data):
         d = dict(data)
-        meta_dict_key = self.meta_keys or f"{self.ref_image}_{self.meta_key_postfix}"
+        meta_dict_key = self.meta_keys
+        if not meta_dict_key:
+            candidate_meta_key = f"{self.ref_image}_{self.meta_key_postfix}"
+            meta_dict = d.get(candidate_meta_key, None)
+            if meta_dict is None:
+                meta_dict_key = get_meta_dict_name(self.ref_image, d)
         if meta_dict_key not in d:
             raise RuntimeError(f"Missing meta_dict {meta_dict_key} in data!")
         if "spatial_shape" not in d[meta_dict_key]:
@@ -742,7 +748,10 @@ def __init__(
     def __call__(self, data: Any) -> dict:
         d = dict(data)
         guidance = d[self.guidance]
-        meta_dict: dict = d[self.meta_keys or f"{self.ref_image}_{self.meta_key_postfix}"]
+        # meta_dict: dict = d[self.meta_keys or f"{self.ref_image}_{self.meta_key_postfix}"]
+        meta_dict: dict = d.get(self.meta_keys or f"{self.ref_image}_{self.meta_key_postfix}", None)
+        if meta_dict is None:
+            meta_dict = d[get_meta_dict_name(self.ref_image, d)]
         current_shape = d[self.ref_image].shape[1:]
         cropped_shape = meta_dict[self.cropped_shape_key][1:]
         factor = np.divide(current_shape, cropped_shape)
@@ -852,7 +861,8 @@ def __init__(
 
     def __call__(self, data: Any) -> dict:
         d = dict(data)
-        meta_dict: dict = d[f"{self.ref_image}_{self.meta_key_postfix}"]
+        meta_dict: dict = d.get(f"{self.ref_image}_{self.meta_key_postfix}",
+                                d[get_meta_dict_name(self.ref_image, d)])
 
         for key, mode, align_corners, meta_key in self.key_iterator(d, self.mode, self.align_corners, self.meta_keys):
             image = d[key]
@@ -969,5 +979,8 @@ def __call__(self, data):
         for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):
             img_slice, idx = self._apply(d[key], guidance)
             d[key] = img_slice
-            d[meta_key or f"{key}_{meta_key_postfix}"]["slice_idx"] = idx
+            # d[meta_key or f"{key}_{meta_key_postfix}"]["slice_idx"] = idx
+            if meta_key not in d:
+                meta_key = get_meta_dict_name(key, d)
+            d[meta_key] = idx
         return d

monai/apps/detection/transforms/dictionary.py

@@ -41,6 +41,7 @@
 from monai.config import KeysCollection, SequenceStr
 from monai.config.type_definitions import DtypeLike, NdarrayOrTensor
 from monai.data.box_utils import COMPUTE_DTYPE, BoxMode, clip_boxes_to_image
+from monai.data.meta_obj import get_meta_dict_name
 from monai.data.meta_tensor import MetaTensor, get_track_meta
 from monai.data.utils import orientation_ras_lps
 from monai.transforms import Flip, RandFlip, RandZoom, Rotate90, SpatialCrop, Zoom
@@ -308,7 +309,9 @@ def extract_affine(self, data: Mapping[Hashable, torch.Tensor]) -> tuple[Ndarray
         elif meta_key in d:
             meta_dict = d[meta_key]
         else:
-            raise ValueError(f"{meta_key} is not found. Please check whether it is the correct the image meta key.")
+            meta_key = get_meta_dict_name(self.box_ref_image_keys, d)
+            if meta_key not in d:
+                raise ValueError(f"{self.image_meta_key} is not found. Please check whether it is the correct the image meta key.")
         if "affine" not in meta_dict:
             raise ValueError(
                 f"'affine' is not found in {meta_key}. \

monai/data/meta_obj.py

@@ -242,3 +242,11 @@ def is_batch(self) -> bool:
     def is_batch(self, val: bool) -> None:
         """Set whether object is part of batch or not."""
         self._is_batch = val
+
+
+def get_meta_dict_name(key, dictionary):
+    for kv, kd in dictionary.items():
+        if isinstance(kd, dict):
+            if kd.get("tensor_name", None) == key:
+                return kv
+    return None

monai/transforms/intensity/dictionary.py

@@ -24,7 +24,7 @@
 
 from monai.config import DtypeLike, KeysCollection
 from monai.config.type_definitions import NdarrayOrTensor
-from monai.data.meta_obj import get_track_meta
+from monai.data.meta_obj import get_meta_dict_name, get_track_meta
 from monai.transforms.intensity.array import (
     AdjustContrast,
     ClipIntensityPercentiles,
@@ -358,6 +358,8 @@ def __call__(self, data) -> dict[Hashable, NdarrayOrTensor]:
             d, self.factor_key, self.meta_keys, self.meta_key_postfix
         ):
             meta_key = meta_key or f"{key}_{meta_key_postfix}"
+            if meta_key not in d:
+                meta_key = get_meta_dict_name(key, d)
             factor: float | None = d[meta_key].get(factor_key) if meta_key in d else None
             offset = None if factor is None else self.shifter.offset * factor
             d[key] = self.shifter(d[key], offset=offset)

monai/transforms/io/dictionary.py

@@ -169,6 +169,7 @@ def __call__(self, data, reader: ImageReader | None = None):
                         f"loader must return a tuple or list (because image_only=False was used), got {type(data)}."
                     )
                 d[key] = data[0]
+                data[1]['tensor_name'] = key
                 if not isinstance(data[1], dict):
                     raise ValueError(f"metadata must be a dict, got {type(data[1])}.")
                 meta_key = meta_key or f"{key}_{meta_key_postfix}"

monai/transforms/meta_utility/dictionary.py

@@ -23,6 +23,7 @@
 import torch
 
 from monai.config.type_definitions import KeysCollection, NdarrayOrTensor
+from monai.data.meta_obj import get_meta_dict_name
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms.inverse import InvertibleTransform
 from monai.transforms.transform import MapTransform
@@ -77,7 +78,10 @@ def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, Nd
             _ = self.get_most_recent_transform(d, key)
             # do the inverse
             im = d[key]
-            meta = d.pop(PostFix.meta(key), None)
+            if PostFix.meta(key) in d:
+                meta = d.pop(PostFix.meta(key), None)
+            else:
+                meta = d.pop(get_meta_dict_name(key, d))
             transforms = d.pop(PostFix.transforms(key), None)
             im = MetaTensor(im, meta=meta, applied_operations=transforms)  # type: ignore
             d[key] = im
@@ -101,6 +105,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         for key in self.key_iterator(d):
             self.push_transform(d, key)
             im = d[key]
+
             meta = d.pop(PostFix.meta(key), None)
             transforms = d.pop(PostFix.transforms(key), None)
             im = MetaTensor(im, meta=meta, applied_operations=transforms)  # type: ignore

monai/transforms/post/dictionary.py

@@ -28,6 +28,7 @@
 from monai import config
 from monai.config.type_definitions import KeysCollection, NdarrayOrTensor, PathLike
 from monai.data.csv_saver import CSVSaver
+from monai.data.meta_obj import get_meta_dict_name
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms.inverse import InvertibleTransform
 from monai.transforms.post.array import (
@@ -797,6 +798,8 @@ def __call__(self, data):
             if meta_key is None and meta_key_postfix is not None:
                 meta_key = f"{key}_{meta_key_postfix}"
             meta_data = d[meta_key] if meta_key is not None else None
+            if meta_data is None:
+                meta_data = d.get(get_meta_dict_name(key, d), None)
             self.saver.save(data=d[key], meta_data=meta_data)
             if self.flush:
                 self.saver.finalize()

monai/transforms/utils.py

@@ -27,6 +27,7 @@
 import monai
 from monai.config import DtypeLike, IndexSelection
 from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
+from monai.data.meta_obj import get_meta_dict_name
 from monai.data.utils import to_affine_nd
 from monai.networks.layers import GaussianFilter
 from monai.networks.utils import meshgrid_ij
@@ -2143,7 +2144,11 @@ def sync_meta_info(key, data_dict, t: bool = True):
     d = dict(data_dict)
 
     # update meta dicts
-    meta_dict_key = PostFix.meta(key)
+    default_meta_dict_key = PostFix.meta(key)
+    if default_meta_dict_key not in d:
+        meta_dict_key = get_meta_dict_name(key, d)
+    else:
+        meta_dict_key = default_meta_dict_key
     if meta_dict_key not in d:
         d[meta_dict_key] = monai.data.MetaTensor.get_default_meta()
     if not isinstance(d[key], monai.data.MetaTensor):