Source code for monai.apps.pathology.utils

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from typing import Any

import numpy as np
import torch

from import ProbNMS
from monai.utils import optional_import

measure, _ = optional_import("skimage.measure")
ndimage, _ = optional_import("scipy.ndimage")

[docs] 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
[docs] def compute_isolated_tumor_cells(tumor_mask: np.ndarray, threshold: float) -> list[int]: """ This method computes identifies Isolated Tumor Cells (ITC) and return their labels. Args: tumor_mask: the tumor mask. threshold: the threshold (at the mask level) to define an isolated tumor cell (ITC). A region with the longest diameter less than this threshold is considered as an ITC. """ max_label = np.amax(tumor_mask) properties = measure.regionprops(tumor_mask) itc_list = [i + 1 for i in range(max_label) if properties[i].major_axis_length < threshold] return itc_list
[docs] class PathologyProbNMS(ProbNMS): """ This class extends monai.utils.ProbNMS and add the `resolution` option for Pathology. """ def __call__(self, probs_map: np.ndarray | torch.Tensor, resolution_level: int = 0) -> list[list]: """ probs_map: the input probabilities map, it must have shape (H[, W, ...]). resolution_level: the level at which the probabilities map is made. """ resolution = pow(2, resolution_level) org_outputs = ProbNMS.__call__(self, probs_map) outputs = [] for org_output in org_outputs: prob = org_output[0] coord = np.asarray(org_output[1:]) coord_wsi = ((coord + 0.5) * resolution).astype(int) outputs.append([prob] + list(coord_wsi)) return outputs