Source code for monai.handlers.surface_distance

# 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.
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from typing import Callable, Union

import torch

from monai.handlers.iteration_metric import IterationMetric
from monai.metrics import SurfaceDistanceMetric
from monai.utils import MetricReduction

[docs]class SurfaceDistance(IterationMetric): """ Computes surface distance from full size Tensor and collects average over batch, class-channels, iterations. """ def __init__( self, include_background: bool = False, symmetric: bool = False, distance_metric: str = "euclidean", 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``. 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"``. 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: 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=MetricReduction.NONE, ) super().__init__( metric_fn=metric_fn, output_transform=output_transform, device=device, save_details=save_details, )