diff --git a/scripts/README.md b/scripts/README.md
new file mode 100644
index 0000000..79fdc9e
--- /dev/null
+++ b/scripts/README.md
@@ -0,0 +1,4 @@
+- VIT-L-LM: {'TRA': 0.9842945483712385, 'DET': 0.9847838957963292}
+- VIT-L: {'TRA': 0.9821987632280266, 'DET': 0.9832445233866193}
+- VIT-L-Specialist': {'TRA': 0.9734016404046677, 'DET': 0.975902901124926}}
+- TRACKMATE: {'TRA': 0.9497270304535693, 'DET': 0.9510953226761397}
diff --git a/scripts/test_ctc_metric.py b/scripts/test_ctc_metric.py
index f3a32ea..6d6cc86 100644
--- a/scripts/test_ctc_metric.py
+++ b/scripts/test_ctc_metric.py
@@ -14,7 +14,8 @@
 
 
 # ROOT = "/scratch/usr/nimanwai/micro-sam/for_tracking/for_traccuracy/"  # hlrn
-ROOT = "/media/anwai/ANWAI/results/micro-sam/for_traccuracy/"  # local
+# ROOT = "/media/anwai/ANWAI/results/micro-sam/for_traccuracy/"  # local
+ROOT = "./for_traccuracy"
 
 
 def mark_potential_split(frames, last_frame, idx):
@@ -34,6 +35,8 @@ def extract_df_from_segmentation(segmentation):
     all_tracks = []
     prev_parent_id = None
 
+    parent_to_children = {}
+
     for idx in track_ids:
         frames = np.unique(np.where(segmentation == idx)[0])
 
@@ -46,6 +49,7 @@ def extract_df_from_segmentation(segmentation):
                 # have fam is the end frame of the potential parent, so our frame has to be the next frame
                 if split_frame + 1 == frames.min():
                     pid = prev_parent_id
+                    parent_to_children[pid] = parent_to_children.get(pid, []) + [idx]
 
                 # otherwise we just have some track that starts so it's not the child
                 else:
@@ -66,6 +70,28 @@ def extract_df_from_segmentation(segmentation):
         all_tracks.append(pd.DataFrame.from_dict([track_dict]))
 
     pred_tracks_df = pd.concat(all_tracks)
+
+    # Remove false positive splits
+    false_parents = [pid for pid, children in parent_to_children.items() if len(children) != 2]
+    pred_tracks_df.loc[np.isin(pred_tracks_df.Parent_ID.values, false_parents), "Parent_ID"] = 0
+
+    # breakpoint()
+
+    parent_ids, n_children = np.unique(pred_tracks_df.Parent_ID.values, return_counts=True)
+    assert (n_children[1:] == 2).all()
+
+    # # For visualization.
+    # import napari
+    # from nifty.tools import takeDict
+    # replace = {cid: (cid if pid == 0 else pid) for cid, pid in zip(pred_tracks_df.Cell_ID, pred_tracks_df.Parent_ID)}
+    # replace[0] = 0
+    # lineage = takeDict(replace, segmentation)
+
+    # v = napari.Viewer()
+    # v.add_labels(segmentation)
+    # v.add_labels(lineage)
+    # napari.run()
+
     return pred_tracks_df
 
 
@@ -84,8 +110,6 @@ def evaluate_tracking(raw, labels, seg, segmentation_method, filter_label_ids):
         _check_ctc(seg_df, seg_nodes, seg)
         seg_T = TrackingGraph(seg_G, segmentation=seg, name=f"DynamicNuclearNet-{segmentation_method}")
 
-    ids, sizes = np.unique(seg_df.Parent_ID.values, return_counts=True)
-    print(ids, sizes)
     # calcuates node attributes for each detectionc
     gt_nodes = _get_node_attributes(labels)
 
@@ -118,6 +142,8 @@ def evaluate_tracking(raw, labels, seg, segmentation_method, filter_label_ids):
 def size_filter(segmentation, min_size=100):
     ids, sizes = np.unique(segmentation, return_counts=True)
     filter_ids = ids[sizes < min_size]
+    # HACK FOR VIT SPECIALIST
+    # filter_ids = np.concatenate([filter_ids, [30, 45]])
     segmentation[np.isin(segmentation, filter_ids)] = 0
     return segmentation
 
@@ -156,7 +182,7 @@ def rectify_labels(instances):
 
             structuring_element = np.zeros((3, 1, 1))
             structuring_element[:, 0, 0] = 1
-       
+
             closed_mask = binary_closing(object_mask.copy(), iterations=1, structure=structuring_element)
             # breakpoint()
             closed_mask = np.logical_or(object_mask, closed_mask)
@@ -193,7 +219,7 @@ def rectify_labels(instances):
 
 
 def main():
-    segmentation_method = "vit_l_lm"
+    segmentation_method = "trackmate_stardist"
 
     raw, labels, segmentation, filter_ids = get_tracking_data(segmentation_method, visualize=False)
     evaluate_tracking(raw, labels, segmentation, segmentation_method, filter_ids)