# Copyright 2020 - 2021 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
from typing import Callable, Optional, Union
import torch
from monai.handlers.iteration_metric import IterationMetric
from monai.metrics import HausdorffDistanceMetric
from monai.utils import MetricReduction
[docs]class HausdorffDistance(IterationMetric):
"""
Computes Hausdorff distance from full size Tensor and collects average over batch, class-channels, iterations.
"""
def __init__(
self,
include_background: bool = False,
distance_metric: str = "euclidean",
percentile: Optional[float] = None,
directed: bool = False,
output_transform: Callable = lambda x: x,
device: Union[str, torch.device] = "cpu",
save_details: bool = True,
) -> None:
"""
Args:
include_background: whether to include distance computation on the first channel of the predicted output.
Defaults to ``False``.
distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
the metric used to compute surface distance. Defaults to ``"euclidean"``.
percentile: an optional float number between 0 and 100. If specified, the corresponding
percentile of the Hausdorff Distance rather than the maximum result will be achieved.
Defaults to ``None``.
directed: whether to calculate directed Hausdorff distance. Defaults to ``False``.
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
device: device specification in case of distributed computation usage.
save_details: whether to save metric computation details per image, for example: hausdorff distance
of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key.
"""
super().__init__(output_transform, device=device)
metric_fn = HausdorffDistanceMetric(
include_background=include_background,
distance_metric=distance_metric,
percentile=percentile,
directed=directed,
reduction=MetricReduction.NONE,
)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
device=device,
save_details=save_details,
)