Source code for monai.handlers.surface_distance

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from __future__ import annotations

from import Callable

from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import SurfaceDistanceMetric
from monai.utils import MetricReduction

[docs] class SurfaceDistance(IgniteMetricHandler): """ Computes surface distance from full size Tensor and collects average over batch, class-channels, iterations. """
[docs] def __init__( self, include_background: bool = False, symmetric: bool = False, distance_metric: str = "euclidean", reduction: 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``. symmetric: whether to calculate the symmetric average surface distance between `seg_pred` and `seg_gt`. Defaults to ``False``. distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``] the metric used to compute surface distance. Defaults to ``"euclidean"``. 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:, explanation and usage example are in the tutorial: save_details: whether to save metric computation details per image, for example: surface dice of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. """ metric_fn = SurfaceDistanceMetric( include_background=include_background, symmetric=symmetric, distance_metric=distance_metric, reduction=reduction, ) super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)