# Source code for monai.metrics.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 Sequence
from typing import Any
import numpy as np
import torch
from monai.metrics.utils import do_metric_reduction, get_edge_surface_distance, ignore_background, prepare_spacing
from monai.utils import MetricReduction, convert_data_type
from .metric import CumulativeIterationMetric
[docs]
class SurfaceDistanceMetric(CumulativeIterationMetric):
"""
Compute Surface Distance between two tensors. It can support both multi-classes and multi-labels tasks.
It supports both symmetric and asymmetric surface distance calculation.
Input `y_pred` is compared with ground truth `y`.
`y_preds` is expected to have binarized predictions and `y` should be in one-hot format.
You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.
`y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]).
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
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 mode of reduction to the metrics, will only apply 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.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.
"""
def __init__(
self,
include_background: bool = False,
symmetric: bool = False,
distance_metric: str = "euclidean",
reduction: MetricReduction | str = MetricReduction.MEAN,
get_not_nans: bool = False,
) -> None:
super().__init__()
self.include_background = include_background
self.distance_metric = distance_metric
self.symmetric = symmetric
self.reduction = reduction
self.get_not_nans = get_not_nans
def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor, **kwargs: Any) -> torch.Tensor: # type: ignore[override]
"""
Args:
y_pred: input data to compute, typical segmentation model output.
It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
should be binarized.
y: ground truth to compute the distance. It must be one-hot format and first dim is batch.
The values should be binarized.
kwargs: additional parameters, e.g. ``spacing`` should be passed to correctly compute the metric.
``spacing``: spacing of pixel (or voxel). This parameter is relevant only
if ``distance_metric`` is set to ``"euclidean"``.
If a single number, isotropic spacing with that value is used for all images in the batch. If a sequence of numbers,
the length of the sequence must be equal to the image dimensions.
This spacing will be used for all images in the batch.
If a sequence of sequences, the length of the outer sequence must be equal to the batch size.
If inner sequence has length 1, isotropic spacing with that value is used for all images in the batch,
else the inner sequence length must be equal to the image dimensions. If ``None``, spacing of unity is used
for all images in batch. Defaults to ``None``.
Raises:
ValueError: when `y_pred` has less than three dimensions.
"""
if y_pred.dim() < 3:
raise ValueError("y_pred should have at least three dimensions.")
# compute (BxC) for each channel for each batch
return compute_average_surface_distance(
y_pred=y_pred,
y=y,
include_background=self.include_background,
symmetric=self.symmetric,
distance_metric=self.distance_metric,
spacing=kwargs.get("spacing"),
)
[docs]
def aggregate(
self, reduction: MetricReduction | str | None = None
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
"""
Execute reduction logic for the output of `compute_average_surface_distance`.
Args:
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
"""
data = self.get_buffer()
if not isinstance(data, torch.Tensor):
raise ValueError("the data to aggregate must be PyTorch Tensor.")
# do metric reduction
f, not_nans = do_metric_reduction(data, reduction or self.reduction)
return (f, not_nans) if self.get_not_nans else f
[docs]
def compute_average_surface_distance(
y_pred: np.ndarray | torch.Tensor,
y: np.ndarray | torch.Tensor,
include_background: bool = False,
symmetric: bool = False,
distance_metric: str = "euclidean",
spacing: int | float | np.ndarray | Sequence[int | float | np.ndarray | Sequence[int | float]] | None = None,
) -> torch.Tensor:
"""
This function is used to compute the Average Surface Distance from `y_pred` to `y`
under the default setting.
In addition, if sets ``symmetric = True``, the average symmetric surface distance between
these two inputs will be returned.
The implementation refers to `DeepMind's implementation <https://github.com/deepmind/surface-distance>`_.
Args:
y_pred: input data to compute, typical segmentation model output.
It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
should be binarized.
y: ground truth to compute mean the distance. It must be one-hot format and first dim is batch.
The values should be binarized.
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"``.
spacing: spacing of pixel (or voxel). This parameter is relevant only if ``distance_metric`` is set to ``"euclidean"``.
If a single number, isotropic spacing with that value is used for all images in the batch. If a sequence of numbers,
the length of the sequence must be equal to the image dimensions. This spacing will be used for all images in the batch.
If a sequence of sequences, the length of the outer sequence must be equal to the batch size.
If inner sequence has length 1, isotropic spacing with that value is used for all images in the batch,
else the inner sequence length must be equal to the image dimensions. If ``None``, spacing of unity is used
for all images in batch. Defaults to ``None``.
"""
if not include_background:
y_pred, y = ignore_background(y_pred=y_pred, y=y)
y_pred = convert_data_type(y_pred, output_type=torch.Tensor, dtype=torch.float)[0]
y = convert_data_type(y, output_type=torch.Tensor, dtype=torch.float)[0]
if y.shape != y_pred.shape:
raise ValueError(f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}.")
batch_size, n_class = y_pred.shape[:2]
asd = torch.empty((batch_size, n_class), dtype=torch.float32, device=y_pred.device)
img_dim = y_pred.ndim - 2
spacing_list = prepare_spacing(spacing=spacing, batch_size=batch_size, img_dim=img_dim)
for b, c in np.ndindex(batch_size, n_class):
_, distances, _ = get_edge_surface_distance(
y_pred[b, c],
y[b, c],
distance_metric=distance_metric,
spacing=spacing_list[b],
symmetric=symmetric,
class_index=c,
)
surface_distance = torch.cat(distances)
asd[b, c] = torch.tensor(np.nan) if surface_distance.shape == (0,) else surface_distance.mean()
return convert_data_type(asd, output_type=torch.Tensor, device=y_pred.device, dtype=torch.float)[0]
```