# 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.
import warnings
from typing import Union
import numpy as np
import torch
from monai.metrics.utils import (
do_metric_reduction,
get_mask_edges,
get_surface_distance,
ignore_background,
is_binary_tensor,
)
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 skip 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: Union[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): # type: ignore
"""
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.
Raises:
ValueError: when `y` is not a binarized tensor.
ValueError: when `y_pred` has less than three dimensions.
"""
is_binary_tensor(y_pred, "y_pred")
is_binary_tensor(y, "y")
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,
)
[docs] def aggregate(self, reduction: Union[MetricReduction, str, None] = None): # type: ignore
"""
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: Union[np.ndarray, torch.Tensor],
y: Union[np.ndarray, torch.Tensor],
include_background: bool = False,
symmetric: bool = False,
distance_metric: str = "euclidean",
):
"""
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 skip 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"``.
"""
if not include_background:
y_pred, y = ignore_background(y_pred=y_pred, y=y)
if isinstance(y, torch.Tensor):
y = y.float()
if isinstance(y_pred, torch.Tensor):
y_pred = y_pred.float()
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 = np.empty((batch_size, n_class))
for b, c in np.ndindex(batch_size, n_class):
(edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])
if not np.any(edges_gt):
warnings.warn(f"the ground truth of class {c} is all 0, this may result in nan/inf distance.")
if not np.any(edges_pred):
warnings.warn(f"the prediction of class {c} is all 0, this may result in nan/inf distance.")
surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)
if symmetric:
surface_distance_2 = get_surface_distance(edges_gt, edges_pred, distance_metric=distance_metric)
surface_distance = np.concatenate([surface_distance, surface_distance_2])
asd[b, c] = np.nan if surface_distance.shape == (0,) else surface_distance.mean()
return convert_data_type(asd, torch.Tensor)[0]