Source code for monai.handlers.mean_dice

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from __future__ import annotations

from import Callable

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

[docs] class MeanDice(IgniteMetricHandler): """ Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations. """
[docs] def __init__( self, include_background: bool = True, reduction: MetricReduction | str = MetricReduction.MEAN, num_classes: int | None = None, output_transform: Callable = lambda x: x, save_details: bool = True, return_with_label: bool | list[str] = False, ) -> None: """ Args: include_background: whether to include dice computation on the first channel of the predicted output. Defaults to True. 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. num_classes: number of input channels (always including the background). When this is None, ``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are single-channel class indices and the number of classes is not automatically inferred from data. 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: mean dice of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key. return_with_label: whether to return the metrics with label, only works when reduction is "mean_batch". If `True`, use "label_{index}" as the key corresponding to C channels; if 'include_background' is True, the index begins at "0", otherwise at "1". It can also take a list of label names. The outcome will then be returned as a dictionary. See also: :py:meth:`monai.metrics.meandice.compute_dice` """ metric_fn = DiceMetric( include_background=include_background, reduction=reduction, num_classes=num_classes, return_with_label=return_with_label, ) super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)