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| from __future__ import annotations |
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| from typing import Any |
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| import numpy as np |
| import torch |
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| from monai.transforms.post.array import ProbNMS |
| from monai.utils import optional_import |
|
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| measure, _ = optional_import("skimage.measure") |
| ndimage, _ = optional_import("scipy.ndimage") |
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| 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 |
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| filled_image = ndimage.morphology.binary_fill_holes(binary) |
| multi_instance_mask = measure.label(filled_image, connectivity=2) |
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| return multi_instance_mask |
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| 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] |
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| return itc_list |
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|
| class PathologyProbNMS(ProbNMS): |
| """ |
| This class extends monai.utils.ProbNMS and add the `resolution` option for |
| Pathology. |
| """ |
|
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| 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 |
|
|
