diff --git a/nodes/mask_nodes.py b/nodes/mask_nodes.py
index b7f1161..8852d06 100644
--- a/nodes/mask_nodes.py
+++ b/nodes/mask_nodes.py
@@ -1347,7 +1347,7 @@ class SeparateMasks:
         return best_approx.squeeze() if best_approx is not None else hull.squeeze()
 
     def separate(self, mask: torch.Tensor, size_threshold_width: int, size_threshold_height: int, max_poly_points: int, mode: str):
-        from scipy.ndimage import label
+        from scipy.ndimage import label, center_of_mass
         import numpy as np
         
         B, H, W = mask.shape
@@ -1360,6 +1360,7 @@ class SeparateMasks:
             structure = np.ones((3, 3), dtype=np.int8)
             labeled, ncomponents = label(mask_np, structure=structure)
             pbar = ProgressBar(ncomponents)
+            
             for component in range(1, ncomponents + 1):
                 component_mask_np = (labeled == component).astype(np.uint8)
                 
@@ -1370,20 +1371,25 @@ class SeparateMasks:
                 
                 width = x_max - x_min + 1
                 height = y_max - y_min + 1
-                print(f"Component {component}: width={width}, height={height}")
+                centroid_x = (x_min + x_max) / 2  # Calculate x centroid
+                print(f"Component {component}: width={width}, height={height}, x_pos={centroid_x}")
                 
                 if width >= size_threshold_width and height >= size_threshold_height:
-                    polygon = self.get_mask_polygon(component_mask_np, max_poly_points)
-                    if mode != "area" and polygon is not None:
-                        poly_mask = self.polygon_to_mask(polygon, (H, W))
-                        poly_mask = torch.tensor(poly_mask, device=mask.device)
-                        separated.append(poly_mask)
-                    elif mode == "area":
+                    if mode != "area":
+                        polygon = self.get_mask_polygon(component_mask_np, max_poly_points)
+                        if polygon is not None:
+                            poly_mask = self.polygon_to_mask(polygon, (H, W))
+                            poly_mask = torch.tensor(poly_mask, device=mask.device)
+                            separated.append((centroid_x, poly_mask))
+                    else:
                         area_mask = torch.tensor(component_mask_np, device=mask.device)
-                        separated.append(area_mask)
+                        separated.append((centroid_x, area_mask))
                 pbar.update(1)
         
         if len(separated) > 0:
+            # Sort by x position and extract only the masks
+            separated.sort(key=lambda x: x[0])
+            separated = [x[1] for x in separated]
             out_masks = torch.stack(separated, dim=0)
             return out_masks,
         else: