diff --git a/sample-apps/radiology/lib/infers/deepedit.py b/sample-apps/radiology/lib/infers/deepedit.py
index e380d6fa9..cda4af2e5 100644
--- a/sample-apps/radiology/lib/infers/deepedit.py
+++ b/sample-apps/radiology/lib/infers/deepedit.py
@@ -11,6 +11,7 @@
 
 from typing import Callable, Sequence, Union
 
+from lib.transforms.transforms import OrientationGuidanceMultipleLabelDeepEditd
 from monai.apps.deepedit.transforms import (
     AddGuidanceFromPointsDeepEditd,
     AddGuidanceSignalDeepEditd,
@@ -87,6 +88,7 @@ def pre_transforms(self, data=None):
         if self.type == InferType.DEEPEDIT:
             t.extend(
                 [
+                    OrientationGuidanceMultipleLabelDeepEditd(ref_image="image", label_names=self.labels),
                     AddGuidanceFromPointsDeepEditd(ref_image="image", guidance="guidance", label_names=self.labels),
                     Resized(keys="image", spatial_size=self.spatial_size, mode="area"),
                     ResizeGuidanceMultipleLabelDeepEditd(guidance="guidance", ref_image="image"),
diff --git a/sample-apps/radiology/lib/transforms/transforms.py b/sample-apps/radiology/lib/transforms/transforms.py
index bc0e6719a..e04471338 100644
--- a/sample-apps/radiology/lib/transforms/transforms.py
+++ b/sample-apps/radiology/lib/transforms/transforms.py
@@ -14,6 +14,7 @@
 
 import numpy as np
 import torch
+from einops import rearrange
 from monai.config import KeysCollection, NdarrayOrTensor
 from monai.transforms import CropForeground, GaussianSmooth, Randomizable, Resize, ScaleIntensity, SpatialCrop
 from monai.transforms.transform import MapTransform, Transform
@@ -505,3 +506,36 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
             if d.get(cache_key) is None:
                 d[cache_key] = copy.deepcopy(d[key])
         return d
+
+
+class OrientationGuidanceMultipleLabelDeepEditd(Transform):
+    def __init__(self, ref_image: str, label_names=None):
+        """
+        Convert the guidance to the RAS orientation
+        """
+        self.ref_image = ref_image
+        self.label_names = label_names
+
+    def transform_points(self, point, affine):
+        """transform point to the coordinates of the transformed image
+        point: numpy array [bs, N, 3]
+        """
+        bs, N = point.shape[:2]
+        point = np.concatenate((point, np.ones((bs, N, 1))), axis=-1)
+        point = rearrange(point, "b n d -> d (b n)")
+        point = affine @ point
+        point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3]
+        return point
+
+    def __call__(self, data):
+        d: Dict = dict(data)
+        for key_label in self.label_names.keys():
+            points = d.get(key_label, [])
+            if len(points) < 1:
+                continue
+            reoriented_points = self.transform_points(
+                np.array(points)[None],
+                np.linalg.inv(d[self.ref_image].meta["affine"].numpy()) @ d[self.ref_image].meta["original_affine"],
+            )
+            d[key_label] = reoriented_points[0]
+        return d
diff --git a/sample-apps/radiology/main.py b/sample-apps/radiology/main.py
index f8ed414ec..e34ccbb6a 100644
--- a/sample-apps/radiology/main.py
+++ b/sample-apps/radiology/main.py
@@ -287,7 +287,7 @@ def main():
 
     parser = argparse.ArgumentParser()
     parser.add_argument("-s", "--studies", default=studies)
-    parser.add_argument("-m", "--model", default="segmentation_spleen")
+    parser.add_argument("-m", "--model", default="deepedit")
     parser.add_argument("-t", "--test", default="infer", choices=("train", "infer"))
     args = parser.parse_args()
 
@@ -308,7 +308,9 @@ def main():
 
         # Run on all devices
         for device in device_list():
-            res = app.infer(request={"model": args.model, "image": image_id, "device": device})
+            res = app.infer(
+                request={"model": args.model, "image": image_id, "device": device, "spleen": [[6, 6, 6], [9, 9, 9]]}
+            )
             # res = app.infer(
             #     request={"model": "vertebra_pipeline", "image": image_id, "device": device, "slicer": False}
             # )