code: def correct_minor_errors(binary_img):
      """
      Remove any small mask chunks or holes
      that could be in the segmentation output.
  
      Parameters
      ----------
      binary_img : np.ndarray
          Binary image
  
      Returns
      -------
      largest_img : np.ndarray
      """
      largest_img = np.zeros_like(binary_img)
      chunks, n_chunk = label(np.abs(1 - binary_img))
      u, c = np.unique(chunks[chunks != 0], return_counts=True)
      target = u[np.argmax(c)]
      largest_img = np.where(chunks == target, 0, 1)
  
      chunks, n_chunk = label(largest_img)
      u, c = np.unique(chunks[chunks != 0], return_counts=True)
      target = u[np.argmax(c)]
      largest_img = np.where(chunks == target, 1, 0)
  
      for x in range(largest_img.shape[0]):
          chunks, n_chunk = label(np.abs(1 - largest_img[x]))
          u, c = np.unique(chunks[chunks != 0], return_counts=True)
          target = u[np.argmax(c)]
          largest_img[x] = np.where(chunks == target, 0, 1)
      for y in range(largest_img.shape[1]):
          chunks, n_chunk = label(np.abs(1 - largest_img[:, y]))
          u, c = np.unique(chunks[chunks != 0], return_counts=True)
          target = u[np.argmax(c)]
          largest_img[:, y] = np.where(chunks == target, 0, 1)
      for z in range(largest_img.shape[2]):
          chunks, n_chunk = label(np.abs(1 - largest_img[..., z]))
          u, c = np.unique(chunks[chunks != 0], return_counts=True)
          target = u[np.argmax(c)]
          largest_img[..., z] = np.where(chunks == target, 0, 1)
  
      return largest_img
  
file_path: dipy/dipy/nn/utils.py
imports: import numpy
  from scipy.ndimage import affine_transform, label
  from dipy.align.reslice import reslice
label_desc: Use image node_id 6 for calling a global function with example usage: correct_minor_errors(binary_img) and returns: largest_img