diff --git a/cc3d/__init__.py b/cc3d/__init__.py
index 93980ee..033cf92 100644
--- a/cc3d/__init__.py
+++ b/cc3d/__init__.py
@@ -71,10 +71,23 @@ def largest_k(
   delta:Union[int,float] = 0,
   return_N:bool = False,
   binary_image:bool = False,
+  precomputed_ccl:bool = False,
 ) -> np.ndarray:
   """
   Returns the k largest connected components
   in the image.
+
+  k: number of components to return (>= 0)
+  connectivity: 
+    (2d) 4 [edges], 8 [edges+corners] 
+    (3d) 6 [faces], 18 [faces+edges], or 26 [faces+edges+corners]
+  delta: if using a continuous image, the allowed difference
+    in adjacent voxel values
+  return_N: return value is (image, N)
+  binary_image: treat the input image as a binary image
+  precomputed_ccl: for performance, avoid computing a CCL
+    pass since the input is already a CCL output from this
+    library.
   """
   assert k >= 0
 
@@ -82,28 +95,34 @@ def largest_k(
 
   if k == 0:
     return np.zeros(img.shape, dtype=np.uint16, order=order)
+  
+  if precomputed_ccl:
+    cc_labels = np.copy(img, order="F")
+    N = np.max(cc_labels)
+  else:
+    cc_labels, N = connected_components(
+      img, connectivity=connectivity, 
+      return_N=True, delta=delta,
+      binary_image=bool(binary_image),
+    )
 
-  cc_labels, N = connected_components(
-    img, connectivity=connectivity, 
-    return_N=True, delta=delta,
-    binary_image=bool(binary_image),
-  )
   if N <= k:
     if return_N:
       return cc_labels, N
     return cc_labels
 
   cts = statistics(cc_labels, no_slice_conversion=True)["voxel_counts"]  
-  preserve = [ (i,ct) for i,ct in enumerate(cts) if i > 0 ]
-  preserve.sort(key=lambda x: x[1])
-  preserve = [ x[0] for x in preserve[-k:] ]
-
+  
   if k == 1:
-    cc_out = (cc_labels == preserve[0])
+    cc_out = (cc_labels == np.argmax(cts))
     if return_N:
       return cc_out, 1
     return cc_out
-  
+
+  preserve = [ (i,ct) for i,ct in enumerate(cts) if i > 0 ]
+  preserve.sort(key=lambda x: x[1])
+  preserve = [ x[0] for x in preserve[-k:] ]
+
   try:
     import fastremap
     cc_out = fastremap.mask_except(cc_labels, preserve, in_place=True)