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centerline_extraction.py

@@ -1024,8 +1024,10 @@ def parse_segments(centerlines):
         # Filter zero-length segments (noise)
         length = calculate_length(segment_points)
         if length > 0.0:
-            point_to_segments[start_id].append(seg_idx)
-            point_to_segments[end_id].append(seg_idx)
+            # Map ALL points to their containing segment (not just start/end)
+            # This ensures bifurcation points (interior to cells) can find connected segments in identify_abdominal_arteries
+            for pt_entry in segment_points:
+                point_to_segments[pt_entry['id']].append(seg_idx)
             segments.append({
                 'label': 'Aorta',
                 'points': segment_points,

server.py

@@ -61,6 +61,16 @@ def extract():
     # Extract labelled segments and artery bifurcation data
     data = extract_centerline_data(centerlines)
 
+    # Include the NRRD image's physical extent so the frontend can normalize the centerline
+    dims = image.GetDimensions()
+    spacing = image.GetSpacing()
+    phys_size = [dims[i] * spacing[i] for i in range(3)]
+    phys_center = [origin[i] + phys_size[i] / 2.0 for i in range(3)]
+    data['volume_info'] = {
+        'center': {'x': phys_center[0], 'y': phys_center[1], 'z': phys_center[2]},
+        'size':   {'x': phys_size[0],   'y': phys_size[1],   'z': phys_size[2]},
+    }
+
     return jsonify({"status": "success", "data": data})
 
   except Exception as e: