diff --git a/src/spatialdata_io/readers/xenium.py b/src/spatialdata_io/readers/xenium.py
index 32455175..6b946ff8 100644
--- a/src/spatialdata_io/readers/xenium.py
+++ b/src/spatialdata_io/readers/xenium.py
@@ -384,6 +384,9 @@ def filter(self, record: logging.LogRecord) -> bool:
 
     if table is not None:
         tables["table"] = table
+    #     valid_nucleus_mask = ~table.obs[XeniumKeys.CELL_ID].isin(invalid_nuc_ids)
+    #     valid_cell_mask = ~table.obs[XeniumKeys.CELL_ID].isin(invalid_cell_ids)
+    #     tables["table"] = table[valid_nucleus_mask & valid_cell_mask].copy()
 
     elements_dict = {
         "images": images,
@@ -418,19 +421,38 @@ def _get_polygons(
     n_jobs: int,
     idx: ArrayLike | None = None,
 ) -> GeoDataFrame:
-    def _poly(arr: ArrayLike) -> Polygon:
-        return Polygon(arr[:-1])
-
     # seems to be faster than pd.read_parquet
     df = pq.read_table(path / file).to_pandas()
 
+    # df[XeniumKeys.CELL_ID] = _decode_cell_id_column(df[XeniumKeys.CELL_ID])
+    # # filter out cell ids with too few vertices to form a valid polygon.
+    # invalid_ids = df.groupby(XeniumKeys.CELL_ID).filter(lambda x: len(x) < 3)[
+    #     XeniumKeys.CELL_ID].unique()
+    # invalid_ids = [] if len(invalid_ids) == 0 else invalid_ids
+    #
+    # if len(invalid_ids) > 0:
+    #     logging.warning(
+    #         f"Found {len(invalid_ids)} invalid polygons for {file}, removing the masks corresponding to the IDs: {invalid_ids}"
+    #     )
+    #
+    # # Filter based on valid cell IDs if idx is provided
+    # if idx is not None:
+    #     idx = idx[~idx.isin(invalid_ids)]
+    #     if len(invalid_ids) > 0:
+    #         idx = idx.reset_index(drop=True)
+    #     df = df[df[XeniumKeys.CELL_ID].isin(idx)]
+    # else:
+    #     # If no idx provided, just (potentially) filter out invalid IDs
+    #     df = df[~df[XeniumKeys.CELL_ID].isin(invalid_ids)]
+
     group_by = df.groupby(XeniumKeys.CELL_ID)
     index = pd.Series(group_by.indices.keys())
     # convert the index to str since we will compare it with an AnnData object, where the index is a str
     index.index = index.index.astype(str)
     index = _decode_cell_id_column(index)
+
     out = Parallel(n_jobs=n_jobs)(
-        delayed(_poly)(i.to_numpy())
+        delayed(Polygon)(i.to_numpy())
         for _, i in group_by[[XeniumKeys.BOUNDARIES_VERTEX_X, XeniumKeys.BOUNDARIES_VERTEX_Y]]
     )
     geo_df = GeoDataFrame({"geometry": out})