# Source code for monai.handlers.surface_distance

```
# Copyright (c) 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from collections.abc 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:
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: 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)
```