diff --git a/e2e/volumes/test_parcellationmap.py b/e2e/volumes/test_parcellationmap.py
index 464d6415f..22ed92b5b 100644
--- a/e2e/volumes/test_parcellationmap.py
+++ b/e2e/volumes/test_parcellationmap.py
@@ -6,6 +6,7 @@
 from siibra.volumes.volume import Subvolume
 
 from itertools import product
+import numpy as np
 
 maps_to_compress = [
     siibra.get_map("2.9", "mni152"),  # contains fragments
@@ -123,3 +124,19 @@ def test_fetching_merged_volume():
     mp = siibra.get_map("julich 2.9", "bigbrain")
     assert len(mp) > 1
     _ = mp.fetch()
+
+
+@pytest.mark.parametrize("siibramap", [
+    siibra.get_map('julich 3', 'fsaverage'),
+    siibra.get_map('julich 3', 'mni152'),
+])
+def test_fetching_mask(siibramap: Map):
+    for fmt in siibramap.formats:
+        for region in siibramap.regions:
+            mesh_or_img = siibramap.fetch(region, format=fmt)
+            arr = mesh_or_img['labels'] if isinstance(mesh_or_img, dict) else mesh_or_img.dataobj
+            unq_vals = np.unique(arr)
+            assert np.array_equal(unq_vals, [0, 1]), (
+                f"{siibramap}, {fmt}. Mask for {region} should only contain 0 "
+                f"and 1 but found: {unq_vals}"
+            )
diff --git a/siibra/volumes/providers/gifti.py b/siibra/volumes/providers/gifti.py
index b5f4d545f..ae9535917 100644
--- a/siibra/volumes/providers/gifti.py
+++ b/siibra/volumes/providers/gifti.py
@@ -134,14 +134,17 @@ def __init__(self, url: Union[str, dict]):
         else:
             raise NotImplementedError(f"Urls for {self.__class__.__name__} are expected to be of type str or dict.")
 
-    def fetch(self, fragment: str = None, **kwargs):
+    def fetch(self, fragment: str = None, label: int = None, **kwargs):
         """Returns a 1D numpy array of label indices."""
         labels = []
         for fragment_name, loader in self._loaders.items():
             if fragment is not None and fragment.lower() not in fragment_name.lower():
                 continue
             assert len(loader.data.darrays) == 1
-            labels.append(loader.data.darrays[0].data)
+            if label is not None:
+                labels.append((loader.data.darrays[0].data == label).astype('uint8'))
+            else:
+                labels.append(loader.data.darrays[0].data)
 
         return {"labels": np.hstack(labels)}