diff --git a/acvl_utils/cropping_and_padding/bounding_boxes.py b/acvl_utils/cropping_and_padding/bounding_boxes.py
index 1351bee..0fb63ef 100644
--- a/acvl_utils/cropping_and_padding/bounding_boxes.py
+++ b/acvl_utils/cropping_and_padding/bounding_boxes.py
@@ -44,11 +44,18 @@ def bounding_box_to_slice(bounding_box: List[List[int]]):
     return tuple([slice(*i) for i in bounding_box])
 
 
+def crop_to_bbox(array: np.ndarray, bounding_box: List[List[int]]):
+    assert len(bounding_box) == len(array.shape), f"Dimensionality of bbox and array do not match. bbox has length " \
+                                          f"{len(bounding_box)} while array has dimension {len(array.shape)}"
+    slicer = bounding_box_to_slice(bounding_box)
+    return array[slicer]
+
+
 def get_bbox_from_mask(mask: np.ndarray) -> List[List[int]]:
     """
     this implementation uses less ram than the np.where one and is faster as well IF we expect the bounding box to
     be close to the image size. If it's not it's likely slower!
-    
+
     bbox is returned so that you can just do slice(minzidx, maxzidx) to retrieve the object of interest with nothing cut off
 
     :param mask: