Source code for monai.metrics.surface_distance

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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
from monai.utils import MetricReduction


[docs]class SurfaceDistanceMetric: """ 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` (BNHW[D] where N is number of classes) is compared with ground truth `y` (BNHW[D]). `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. 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: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, ``"mean_channel"``, ``"sum_channel"``} Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``. """ def __init__( self, include_background: bool = False, symmetric: bool = False, distance_metric: str = "euclidean", reduction: Union[MetricReduction, str] = MetricReduction.MEAN, ) -> None: super().__init__() self.include_background = include_background self.distance_metric = distance_metric self.symmetric = symmetric self.reduction = reduction def __call__(self, y_pred: torch.Tensor, y: torch.Tensor): """ 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. """ if not torch.all(y_pred.byte() == y_pred): warnings.warn("y_pred is not a binarized tensor here!") if not torch.all(y.byte() == y): raise ValueError("y should be a binarized tensor.") dims = y_pred.ndimension() if dims < 3: raise ValueError("y_pred should have at least three dimensions.") # compute (BxC) for each channel for each batch f = compute_average_surface_distance( y_pred=y_pred, y=y, include_background=self.include_background, symmetric=self.symmetric, distance_metric=self.distance_metric, ) # do metric reduction f, not_nans = do_metric_reduction(f, self.reduction) return f, not_nans
[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. 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, ) y = y.float() y_pred = y_pred.float() if y.shape != y_pred.shape: raise ValueError("y_pred and y should have same shapes.") 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]) surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric) if surface_distance.shape == (0,): avg_surface_distance = np.nan else: avg_surface_distance = surface_distance.mean() if not symmetric: asd[b, c] = avg_surface_distance else: surface_distance_2 = get_surface_distance(edges_gt, edges_pred, distance_metric=distance_metric) if surface_distance_2.shape == (0,): avg_surface_distance_2 = np.nan else: avg_surface_distance_2 = surface_distance_2.mean() asd[b, c] = np.mean((avg_surface_distance, avg_surface_distance_2)) return torch.from_numpy(asd)