Source code for monai.handlers.hausdorff_distance
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from typing import Callable, Optional, Union
from monai.handlers.ignite_metric import IgniteMetric
from monai.metrics import HausdorffDistanceMetric
from monai.utils import MetricReduction
[docs]class HausdorffDistance(IgniteMetric):
"""
Computes Hausdorff distance from full size Tensor and collects average over batch, class-channels, iterations.
"""
[docs] def __init__(
self,
include_background: bool = False,
distance_metric: str = "euclidean",
percentile: Optional[float] = None,
directed: bool = False,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
output_transform: Callable = lambda x: x,
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``.
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
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.
"""
metric_fn = HausdorffDistanceMetric(
include_background=include_background,
distance_metric=distance_metric,
percentile=percentile,
directed=directed,
reduction=reduction,
)
super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)