Source code for monai.apps.pathology.metrics.lesion_froc

# 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 TYPE_CHECKING, Any, Iterable

import numpy as np

from monai.apps.pathology.utils import PathologyProbNMS, compute_isolated_tumor_cells, compute_multi_instance_mask
from monai.config import NdarrayOrTensor
from import WSIReader
from monai.metrics import compute_fp_tp_probs, compute_froc_curve_data, compute_froc_score
from monai.utils import min_version, optional_import

    from tqdm import tqdm

    has_tqdm = True
    tqdm, has_tqdm = optional_import("tqdm", "4.47.0", min_version, "tqdm")

if not has_tqdm:

    def tqdm(x):
        return x

[docs] class LesionFROC: """ Evaluate with Free Response Operating Characteristic (FROC) score. Args: data: either the list of dictionaries containing probability maps (inference result) and tumor mask (ground truth), as below, or the path to a json file containing such list. `{ "prob_map": "path/to/prob_map_1.npy", "tumor_mask": "path/to/ground_truth_1.tiff", "level": 6, "pixel_spacing": 0.243 }` grow_distance: Euclidean distance (in micrometer) by which to grow the label the ground truth's tumors. Defaults to 75, which is the equivalent size of 5 tumor cells. itc_diameter: the maximum diameter of a region (in micrometer) to be considered as an isolated tumor cell. Defaults to 200. eval_thresholds: the false positive rates for calculating the average sensitivity. Defaults to (0.25, 0.5, 1, 2, 4, 8) which is the same as the CAMELYON 16 Challenge. nms_sigma: the standard deviation for gaussian filter of non-maximal suppression. Defaults to 0.0. nms_prob_threshold: the probability threshold of non-maximal suppression. Defaults to 0.5. nms_box_size: the box size (in pixel) to be removed around the pixel for non-maximal suppression. image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide. Defaults to CuCIM. Note: For more info on `nms_*` parameters look at monai.utils.prob_nms.ProbNMS`. """ def __init__( self, data: list[dict], grow_distance: int = 75, itc_diameter: int = 200, eval_thresholds: tuple = (0.25, 0.5, 1, 2, 4, 8), nms_sigma: float = 0.0, nms_prob_threshold: float = 0.5, nms_box_size: int = 48, image_reader_name: str = "cuCIM", ) -> None: = data self.grow_distance = grow_distance self.itc_diameter = itc_diameter self.eval_thresholds = eval_thresholds self.image_reader = WSIReader(image_reader_name) self.nms = PathologyProbNMS(sigma=nms_sigma, prob_threshold=nms_prob_threshold, box_size=nms_box_size)
[docs] def prepare_inference_result(self, sample: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Prepare the probability map for detection evaluation. """ # load the probability map (the result of model inference) prob_map = np.load(sample["prob_map"]) # apply non-maximal suppression nms_outputs = self.nms(probs_map=prob_map, resolution_level=sample["level"]) # separate nms outputs probs: Iterable[Any] x_coord: Iterable[Any] y_coord: Iterable[Any] if nms_outputs: probs, x_coord, y_coord = zip(*nms_outputs) else: probs, x_coord, y_coord = [], [], [] return np.array(probs), np.array(x_coord), np.array(y_coord)
[docs] def prepare_ground_truth(self, sample): """ Prepare the ground truth for evaluation based on the binary tumor mask """ # load binary tumor masks img_obj =["tumor_mask"]) tumor_mask = self.image_reader.get_data(img_obj, level=sample["level"])[0][0] # calculate pixel spacing at the mask level mask_pixel_spacing = sample["pixel_spacing"] * pow(2, sample["level"]) # compute multi-instance mask from a binary mask grow_pixel_threshold = self.grow_distance / (mask_pixel_spacing * 2) tumor_mask = compute_multi_instance_mask(mask=tumor_mask, threshold=grow_pixel_threshold) # identify isolated tumor cells itc_threshold = (self.itc_diameter + self.grow_distance) / mask_pixel_spacing itc_labels = compute_isolated_tumor_cells(tumor_mask=tumor_mask, threshold=itc_threshold) return tumor_mask, itc_labels
[docs] def compute_fp_tp(self): """ Compute false positive and true positive probabilities for tumor detection, by comparing the model outputs with the prepared ground truths for all samples """ total_fp_probs: list[NdarrayOrTensor] = [] total_tp_probs: list[NdarrayOrTensor] = [] total_num_targets = 0 num_images = len( for sample in tqdm( probs, y_coord, x_coord = self.prepare_inference_result(sample) ground_truth, itc_labels = self.prepare_ground_truth(sample) # compute FP and TP probabilities for a pair of an image and an ground truth mask fp_probs, tp_probs, num_targets = compute_fp_tp_probs( probs=probs, y_coord=y_coord, x_coord=x_coord, evaluation_mask=ground_truth, labels_to_exclude=itc_labels, resolution_level=sample["level"], ) total_fp_probs.extend(fp_probs) total_tp_probs.extend(tp_probs) total_num_targets += num_targets return np.array(total_fp_probs), np.array(total_tp_probs), total_num_targets, num_images
[docs] def evaluate(self): """ Evaluate the detection performance of a model based on the model probability map output, the ground truth tumor mask, and their associated metadata (e.g., pixel_spacing, level) """ # compute false positive (FP) and true positive (TP) probabilities for all images fp_probs, tp_probs, num_targets, num_images = self.compute_fp_tp() # compute FROC curve given the evaluation of all images fps_per_image, total_sensitivity = compute_froc_curve_data( fp_probs=fp_probs, tp_probs=tp_probs, num_targets=num_targets, num_images=num_images ) # compute FROC score give specific evaluation threshold froc_score = compute_froc_score( fps_per_image=fps_per_image, total_sensitivity=total_sensitivity, eval_thresholds=self.eval_thresholds ) return froc_score