monai.transforms.post.array

A collection of “vanilla” transforms for the model output tensors.

Classes

Activations([sigmoid, softmax, other])	Activation operations, typically Sigmoid or Softmax.
AsDiscrete([argmax, to_onehot, threshold, ...])	Convert the input tensor/array into discrete values, possible operations are:
DistanceTransformEDT([sampling])	Applies the Euclidean distance transform on the input.
Ensemble()
FillHoles([applied_labels, connectivity])	This transform fills holes in the image and can be used to remove artifacts inside segments.
Invert([transform, nearest_interp, device, ...])	Utility transform to automatically invert the previously applied transforms.
KeepLargestConnectedComponent([...])	Keeps only the largest connected component in the image.
LabelFilter(applied_labels)	This transform filters out labels and can be used as a processing step to view only certain labels.
LabelToContour([kernel_type])	Return the contour of binary input images that only compose of 0 and 1, with Laplacian kernel set as default for edge detection.
MeanEnsemble([weights])	Execute mean ensemble on the input data.
ProbNMS([spatial_dims, sigma, ...])	Performs probability based non-maximum suppression (NMS) on the probabilities map via iteratively selecting the coordinate with highest probability and then move it as well as its surrounding values.
RemoveSmallObjects([min_size, connectivity, ...])	Use skimage.morphology.remove_small_objects to remove small objects from images.
SobelGradients([kernel_size, spatial_axes, ...])	Calculate Sobel gradients of a grayscale image with the shape of CxH[xWxDx...] or BxH[xWxDx...].
VoteEnsemble([num_classes])	Execute vote ensemble on the input data.