mindspore.nn.MeanSurfaceDistance

class mindspore.nn.MeanSurfaceDistance(symmetric=False, distance_metric='euclidean')

This function is used to compute the Average Surface Distance from y_pred to y under the default setting. Mean Surface Distance(MSD), the mean of the vector is taken. This tell us how much, on average, the surface varies between the segmentation and the GT.

Parameters

• distance_metric (string) – The parameter of calculating Hausdorff distance supports three measurement methods, “euclidean”, “chessboard” or “taxicab”. Default: “euclidean”.

• symmetric (bool) – if calculate the symmetric average surface distance between y_pred and y. In addition, if sets symmetric = True, the average symmetric surface distance between these two inputs will be returned. Defaults: False.

Examples

>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
>>> metric = nn.MeanSurfaceDistance(symmetric=False, distance_metric="euclidean")
>>> metric.clear()
>>> metric.update(x, y, 0)
>>> mean_average_distance = metric.eval()
>>> print(mean_average_distance)
0.8047378541243649

clear()

Clears the internal evaluation result.

eval()

Calculate mean surface distance.

update(*inputs)

Updates the internal evaluation result ‘y_pred’, ‘y’ and ‘label_idx’.

Parameters

inputs –

Input ‘y_pred’, ‘y’ and ‘label_idx’. ‘y_pred’ and ‘y’ are Tensor or numpy.ndarray. ‘y_pred’ is the

predicted binary image. ‘y’ is the actual binary image. ‘label_idx’, the data type of label_idx is int.

Raises:

ValueError: If the number of the inputs is not 3.