diff --git a/cellpose/transforms.py b/cellpose/transforms.py
index 402d0eed..798c0464 100644
--- a/cellpose/transforms.py
+++ b/cellpose/transforms.py
@@ -674,6 +674,45 @@ def normalize_img(img, normalize=True, norm3D=False, invert=False, lowhigh=None,
 
     return img_norm
 
+def resize_safe(img, Ly, Lx, interpolation=cv2.INTER_LINEAR):
+    """OpenCV resize function does not support uint32. 
+    
+    This function converts the image to float32 before resizing and then converts it back to uint32. Not safe!
+    References issue: https://github.com/MouseLand/cellpose/issues/937
+    
+    Implications:
+    * Runtime: Runtime increases by 5x-50x due to type casting. However, with resizing being very efficient, this is not 
+    a big issue. A 10,000x10,000 image takes 0.47s instead of 0.016s to cast and resize on 32 cores on GPU.
+    * Memory: However, memory usage increases. Not tested by how much.
+    
+    Args:
+        img (ndarray): Image of size [Ly x Lx].
+        Ly (int): Desired height of the resized image.
+        Lx (int): Desired width of the resized image.
+        interpolation (int, optional): OpenCV interpolation method. Defaults to cv2.INTER_LINEAR.
+    
+    Returns:
+        ndarray: Resized image of size [Ly x Lx].
+        
+    """
+        
+    # cast image
+    cast = img.dtype == np.uint32
+    if cast:
+        # 
+        img = img.astype(np.float32)
+    
+    # resize
+    img = cv2.resize(img, (Lx, Ly), interpolation=interpolation)
+    
+    # cast back
+    if cast:
+        transforms_logger.warning("resizing image from uint32 to float32 and back to uint32")
+        img = img.round().astype(np.uint32)
+        
+    return img
+
+
 def resize_image(img0, Ly=None, Lx=None, rsz=None, interpolation=cv2.INTER_LINEAR,
                  no_channels=False):
     """Resize image for computing flows / unresize for computing dynamics.
@@ -721,9 +760,9 @@ def resize_image(img0, Ly=None, Lx=None, rsz=None, interpolation=cv2.INTER_LINEA
         else:
             imgs = np.zeros((img0.shape[0], Ly, Lx, img0.shape[-1]), np.float32)
         for i, img in enumerate(img0):
-            imgs[i] = cv2.resize(img, (Lx, Ly), interpolation=interpolation)
+            imgs[i] = resize_safe(img, Ly, Lx, interpolation=interpolation)
     else:
-        imgs = cv2.resize(img0, (Lx, Ly), interpolation=interpolation)
+        imgs = resize_safe(img0, Ly, Lx, interpolation=interpolation)
     return imgs
 
 
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 91f40d7e..10c20c76 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -21,3 +21,22 @@ def test_normalize_img(data_dir):
 
     img_norm = normalize_img(img, norm3D=False, sharpen_radius=8)
     assert img_norm.shape == img.shape
+
+def test_resize(data_dir):
+    img = io.imread(str(data_dir.joinpath('2D').joinpath('rgb_2D_tif.tif')))
+    
+    Lx = 100
+    Ly = 200
+    
+    img8 = resize_image(img.astype("uint8"), Lx=Lx, Ly=Ly)
+    assert img8.shape == (Ly, Lx, 3)
+    assert img8.dtype == np.uint8
+    
+    img16 = resize_image(img.astype("uint16"), Lx=Lx, Ly=Ly)
+    assert img16.shape == (Ly, Lx, 3)
+    assert img16.dtype == np.uint16
+    
+    img32 = resize_image(img.astype("uint32"), Lx=Lx, Ly=Ly)
+    assert img32.shape == (Ly, Lx, 3)
+    assert img32.dtype == np.uint32
+