code: def large_clusters_mask(volume, min_cluster_size):
      
      labels, num_labels = scn.label(volume)
  
      labels_to_keep = set([i for i in range(num_labels)
                           if np.sum(labels == i) >= min_cluster_size])
  
      clusters_mask = np.zeros_like(volume, dtype=int)
      for l in range(num_labels):
          if l in labels_to_keep:
              clusters_mask[labels == l] = 1
  
      return clusters_mask
docs: Return as mask for `volume` that includes only areas where
      the connected components have a size bigger than `min_cluster_size`
      in number of voxels.
  
      Parameters
      -----------
      volume: numpy.array
          3D boolean array.
  
      min_cluster_size: int
          Minimum size in voxels that the connected component must have.
  
      Returns
      --------
      volume: numpy.array
          3D int array with a mask excluding small connected components.