# 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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from typing import Callable
from monai.handlers.ignite_metric import IgniteMetric
from monai.metrics import SurfaceDistanceMetric
from monai.utils import MetricReduction
[docs]class SurfaceDistance(IgniteMetric):
"""
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,
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: 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()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
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.MEAN,
)
super().__init__(
metric_fn=metric_fn,
output_transform=output_transform,
save_details=save_details,
)