code: def compute_multi_instance_mask(
      mask: np.ndarray, threshold: float
  ) -> Any:
      """
      This method computes the segmentation mask according to the binary tumor mask.
  
      Args:
          mask: the binary mask array
          threshold: the threshold to fill holes
      """
  
      neg = 255 - mask * 255
      distance = ndimage.morphology.distance_transform_edt(neg)
      binary = distance < threshold
  
      filled_image = ndimage.morphology.binary_fill_holes(binary)
      multi_instance_mask = measure.label(filled_image, connectivity=2)
  
      return multi_instance_mask
  
file_path: MONAI/monai/apps/pathology/utils.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import torch
  from monai.transforms.post.array import ProbNMS
  from monai.utils import optional_import
label_desc: Use image node_id 1 for calling a global function with example usage: compute_multi_instance_mask(mask, threshold) and returns: multi_instance_mask