# Source code for monai.metrics.meandice

```
# 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 typing import Union
import torch
from monai.metrics.utils import do_metric_reduction, ignore_background, is_binary_tensor
from monai.utils import MetricReduction, deprecated
from .metric import CumulativeIterationMetric
[docs]class DiceMetric(CumulativeIterationMetric):
"""
Compute average Dice score between two tensors. It can support both multi-classes and multi-labels tasks.
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.
The `include_background` parameter can be set to ``False`` to exclude
the first category (channel index 0) which is by convention assumed to be background. If the non-background
segmentations are small compared to the total image size they can get overwhelmed by the signal from the
background.
`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 Dice computation on the first channel of
the predicted output. Defaults to ``True``.
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.
ignore_empty: whether to ignore empty ground truth cases during calculation.
If `True`, NaN value will be set for empty ground truth cases.
If `False`, 1 will be set if the predictions of empty ground truth cases are also empty.
"""
def __init__(
self,
include_background: bool = True,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
get_not_nans: bool = False,
ignore_empty: bool = True,
) -> None:
super().__init__()
self.include_background = include_background
self.reduction = reduction
self.get_not_nans = get_not_nans
self.ignore_empty = ignore_empty
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 mean dice metric. 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")
dims = y_pred.ndimension()
if dims < 3:
raise ValueError(f"y_pred should have at least 3 dimensions (batch, channel, spatial), got {dims}.")
# compute dice (BxC) for each channel for each batch
return compute_dice(
y_pred=y_pred, y=y, include_background=self.include_background, ignore_empty=self.ignore_empty
)
[docs] def aggregate(self, reduction: Union[MetricReduction, str, None] = None):
"""
Execute reduction logic for the output of `compute_meandice`.
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
def compute_dice(
y_pred: torch.Tensor, y: torch.Tensor, include_background: bool = True, ignore_empty: bool = True
) -> torch.Tensor:
"""Computes Dice score metric for a batch of predictions.
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 dice metric. It must be one-hot format and first dim is batch.
The values should be binarized.
include_background: whether to skip Dice computation on the first channel of
the predicted output. Defaults to True.
ignore_empty: whether to ignore empty ground truth cases during calculation.
If `True`, NaN value will be set for empty ground truth cases.
If `False`, 1 will be set if the predictions of empty ground truth cases are also empty.
Returns:
Dice scores per batch and per class, (shape [batch_size, num_classes]).
Raises:
ValueError: when `y_pred` and `y` have different shapes.
"""
if not include_background:
y_pred, y = ignore_background(y_pred=y_pred, y=y)
y = y.float()
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}.")
# reducing only spatial dimensions (not batch nor channels)
n_len = len(y_pred.shape)
reduce_axis = list(range(2, n_len))
intersection = torch.sum(y * y_pred, dim=reduce_axis)
y_o = torch.sum(y, reduce_axis)
y_pred_o = torch.sum(y_pred, dim=reduce_axis)
denominator = y_o + y_pred_o
if ignore_empty:
return torch.where(y_o > 0, (2.0 * intersection) / denominator, torch.tensor(float("nan"), device=y_o.device))
return torch.where(denominator > 0, (2.0 * intersection) / denominator, torch.tensor(1.0, device=y_o.device))
[docs]@deprecated(since="1.0.0", msg_suffix="use `compute_dice` instead.")
def compute_meandice(*args, **kwargs):
return compute_dice(*args, **kwargs)
```