Source code for monai.transforms.post.dictionary

# Copyright 2020 - 2021 MONAI Consortium
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
A collection of dictionary-based wrappers around the "vanilla" transforms for model output tensors
defined in :py:class:`monai.transforms.utility.array`.

Class names are ended with 'd' to denote dictionary-based transforms.
"""

import warnings
from copy import deepcopy
from typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Union

import numpy as np
import torch
from torch.utils.data import DataLoader as TorchDataLoader

from monai.config import KeysCollection
from monai.data.csv_saver import CSVSaver
from monai.data.utils import decollate_batch, no_collation
from monai.transforms.inverse import InvertibleTransform
from monai.transforms.inverse_batch_transform import BatchInverseTransform
from monai.transforms.post.array import (
    Activations,
    AsDiscrete,
    KeepLargestConnectedComponent,
    LabelToContour,
    MeanEnsemble,
    ProbNMS,
    VoteEnsemble,
)
from monai.transforms.transform import MapTransform
from monai.transforms.utility.array import ToTensor
from monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode
from monai.utils import ensure_tuple, ensure_tuple_rep
from monai.utils.enums import InverseKeys

__all__ = [
    "Activationsd",
    "AsDiscreted",
    "KeepLargestConnectedComponentd",
    "LabelToContourd",
    "Ensembled",
    "MeanEnsembled",
    "VoteEnsembled",
    "ActivationsD",
    "ActivationsDict",
    "AsDiscreteD",
    "AsDiscreteDict",
    "InvertD",
    "InvertDict",
    "Invertd",
    "KeepLargestConnectedComponentD",
    "KeepLargestConnectedComponentDict",
    "LabelToContourD",
    "LabelToContourDict",
    "MeanEnsembleD",
    "MeanEnsembleDict",
    "VoteEnsembleD",
    "VoteEnsembleDict",
    "DecollateD",
    "DecollateDict",
    "Decollated",
    "ProbNMSd",
    "ProbNMSD",
    "ProbNMSDict",
    "SaveClassificationd",
    "SaveClassificationD",
    "SaveClassificationDict",
]


[docs]class Activationsd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.AddActivations`. Add activation layers to the input data specified by `keys`. """ def __init__( self, keys: KeysCollection, sigmoid: Union[Sequence[bool], bool] = False, softmax: Union[Sequence[bool], bool] = False, other: Optional[Union[Sequence[Callable], Callable]] = None, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to model output and label. See also: :py:class:`monai.transforms.compose.MapTransform` sigmoid: whether to execute sigmoid function on model output before transform. it also can be a sequence of bool, each element corresponds to a key in ``keys``. softmax: whether to execute softmax function on model output before transform. it also can be a sequence of bool, each element corresponds to a key in ``keys``. other: callable function to execute other activation layers, for example: `other = torch.tanh`. it also can be a sequence of Callable, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys)) self.softmax = ensure_tuple_rep(softmax, len(self.keys)) self.other = ensure_tuple_rep(other, len(self.keys)) self.converter = Activations()
[docs] def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]: d = dict(data) for key, sigmoid, softmax, other in self.key_iterator(d, self.sigmoid, self.softmax, self.other): d[key] = self.converter(d[key], sigmoid, softmax, other) return d
[docs]class AsDiscreted(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.AsDiscrete`. """ def __init__( self, keys: KeysCollection, argmax: Union[Sequence[bool], bool] = False, to_onehot: Union[Sequence[bool], bool] = False, n_classes: Optional[Union[Sequence[int], int]] = None, threshold_values: Union[Sequence[bool], bool] = False, logit_thresh: Union[Sequence[float], float] = 0.5, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to model output and label. See also: :py:class:`monai.transforms.compose.MapTransform` argmax: whether to execute argmax function on input data before transform. it also can be a sequence of bool, each element corresponds to a key in ``keys``. to_onehot: whether to convert input data into the one-hot format. Defaults to False. it also can be a sequence of bool, each element corresponds to a key in ``keys``. n_classes: the number of classes to convert to One-Hot format. it also can be a sequence of int, each element corresponds to a key in ``keys``. threshold_values: whether threshold the float value to int number 0 or 1, default is False. it also can be a sequence of bool, each element corresponds to a key in ``keys``. logit_thresh: the threshold value for thresholding operation, default is 0.5. it also can be a sequence of float, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.argmax = ensure_tuple_rep(argmax, len(self.keys)) self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys)) self.n_classes = ensure_tuple_rep(n_classes, len(self.keys)) self.threshold_values = ensure_tuple_rep(threshold_values, len(self.keys)) self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys)) self.converter = AsDiscrete()
[docs] def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]: d = dict(data) for key, argmax, to_onehot, n_classes, threshold_values, logit_thresh in self.key_iterator( d, self.argmax, self.to_onehot, self.n_classes, self.threshold_values, self.logit_thresh ): d[key] = self.converter( d[key], argmax, to_onehot, n_classes, threshold_values, logit_thresh, ) return d
[docs]class KeepLargestConnectedComponentd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.KeepLargestConnectedComponent`. """ def __init__( self, keys: KeysCollection, applied_labels: Union[Sequence[int], int], independent: bool = True, connectivity: Optional[int] = None, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` applied_labels: Labels for applying the connected component on. If only one channel. The pixel whose value is not in this list will remain unchanged. If the data is in one-hot format, this is the channel indices to apply transform. independent: consider several labels as a whole or independent, default is `True`. Example use case would be segment label 1 is liver and label 2 is liver tumor, in that case you want this "independent" to be specified as False. connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If ``None``, a full connectivity of ``input.ndim`` is used. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.converter = KeepLargestConnectedComponent(applied_labels, independent, connectivity)
[docs] def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]: d = dict(data) for key in self.key_iterator(d): d[key] = self.converter(d[key]) return d
[docs]class LabelToContourd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.LabelToContour`. """ def __init__(self, keys: KeysCollection, kernel_type: str = "Laplace", allow_missing_keys: bool = False) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` kernel_type: the method applied to do edge detection, default is "Laplace". allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.converter = LabelToContour(kernel_type=kernel_type)
[docs] def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]: d = dict(data) for key in self.key_iterator(d): d[key] = self.converter(d[key]) return d
[docs]class Ensembled(MapTransform): """ Base class of dictionary-based ensemble transforms. """ def __init__( self, keys: KeysCollection, ensemble: Callable[[Union[Sequence[torch.Tensor], torch.Tensor]], torch.Tensor], output_key: Optional[str] = None, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be stack and execute ensemble. if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`. output_key: the key to store ensemble result in the dictionary. ensemble: callable method to execute ensemble on specified data. if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default. allow_missing_keys: don't raise exception if key is missing. Raises: TypeError: When ``ensemble`` is not ``callable``. ValueError: When ``len(keys) > 1`` and ``output_key=None``. Incompatible values. """ super().__init__(keys, allow_missing_keys) if not callable(ensemble): raise TypeError(f"ensemble must be callable but is {type(ensemble).__name__}.") self.ensemble = ensemble if len(self.keys) > 1 and output_key is None: raise ValueError("Incompatible values: len(self.keys) > 1 and output_key=None.") self.output_key = output_key if output_key is not None else self.keys[0]
[docs] def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torch.Tensor]: d = dict(data) items: Union[List[torch.Tensor], torch.Tensor] if len(self.keys) == 1: items = d[self.keys[0]] else: items = [d[key] for key in self.key_iterator(d)] d[self.output_key] = self.ensemble(items) return d
[docs]class MeanEnsembled(Ensembled): """ Dictionary-based wrapper of :py:class:`monai.transforms.MeanEnsemble`. """ def __init__( self, keys: KeysCollection, output_key: Optional[str] = None, weights: Optional[Union[Sequence[float], torch.Tensor, np.ndarray]] = None, ) -> None: """ Args: keys: keys of the corresponding items to be stack and execute ensemble. if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`. output_key: the key to store ensemble result in the dictionary. if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default. weights: can be a list or tuple of numbers for input data with shape: [E, B, C, H, W[, D]]. or a Numpy ndarray or a PyTorch Tensor data. the `weights` will be added to input data from highest dimension, for example: 1. if the `weights` only has 1 dimension, it will be added to the `E` dimension of input data. 2. if the `weights` has 3 dimensions, it will be added to `E`, `B` and `C` dimensions. it's a typical practice to add weights for different classes: to ensemble 3 segmentation model outputs, every output has 4 channels(classes), so the input data shape can be: [3, B, 4, H, W, D]. and add different `weights` for different classes, so the `weights` shape can be: [3, 1, 4]. for example: `weights = [[[1, 2, 3, 4]], [[4, 3, 2, 1]], [[1, 1, 1, 1]]]`. """ ensemble = MeanEnsemble(weights=weights) super().__init__(keys, ensemble, output_key)
[docs]class VoteEnsembled(Ensembled): """ Dictionary-based wrapper of :py:class:`monai.transforms.VoteEnsemble`. """ def __init__( self, keys: KeysCollection, output_key: Optional[str] = None, num_classes: Optional[int] = None ) -> None: """ Args: keys: keys of the corresponding items to be stack and execute ensemble. if only 1 key provided, suppose it's a PyTorch Tensor with data stacked on dimension `E`. output_key: the key to store ensemble result in the dictionary. if only 1 key provided in `keys`, `output_key` can be None and use `keys` as default. num_classes: if the input is single channel data instead of One-Hot, we can't get class number from channel, need to explicitly specify the number of classes to vote. """ ensemble = VoteEnsemble(num_classes=num_classes) super().__init__(keys, ensemble, output_key)
class Decollated(MapTransform): """ Decollate a batch of data. Note that unlike most MapTransforms, this will decollate all data, so keys are not needed. Args: detach: whether to detach the tensors. Scalars tensors will be detached into number types instead of torch tensors. """ def __init__(self, keys="", detach: bool = True) -> None: super().__init__(keys=keys) self.detach = detach def __call__(self, data: dict): return decollate_batch(data, detach=self.detach) class ProbNMSd(MapTransform): """ 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. The remove range is determined by the parameter `box_size`. If multiple coordinates have the same highest probability, only one of them will be selected. Args: spatial_dims: number of spatial dimensions of the input probabilities map. Defaults to 2. sigma: the standard deviation for gaussian filter. It could be a single value, or `spatial_dims` number of values. Defaults to 0.0. prob_threshold: the probability threshold, the function will stop searching if the highest probability is no larger than the threshold. The value should be no less than 0.0. Defaults to 0.5. box_size: the box size (in pixel) to be removed around the the pixel with the maximum probability. It can be an integer that defines the size of a square or cube, or a list containing different values for each dimensions. Defaults to 48. Return: a list of selected lists, where inner lists contain probability and coordinates. For example, for 3D input, the inner lists are in the form of [probability, x, y, z]. Raises: ValueError: When ``prob_threshold`` is less than 0.0. ValueError: When ``box_size`` is a list or tuple, and its length is not equal to `spatial_dims`. ValueError: When ``box_size`` has a less than 1 value. """ def __init__( self, keys: KeysCollection, spatial_dims: int = 2, sigma: Union[Sequence[float], float, Sequence[torch.Tensor], torch.Tensor] = 0.0, prob_threshold: float = 0.5, box_size: Union[int, Sequence[int]] = 48, allow_missing_keys: bool = False, ) -> None: super().__init__(keys, allow_missing_keys) self.prob_nms = ProbNMS( spatial_dims=spatial_dims, sigma=sigma, prob_threshold=prob_threshold, box_size=box_size, ) def __call__(self, data: Mapping[Hashable, Union[np.ndarray, torch.Tensor]]): d = dict(data) for key in self.key_iterator(d): d[key] = self.prob_nms(d[key]) return d
[docs]class Invertd(MapTransform): """ Utility transform to automatically invert the previously applied transforms. When applying pre-transforms on a orig_key(like: `image`, `label`, etc.), we record the context information of applied transforms in a dictionary in the input data dictionary with the key "{orig_key}_transforms". This post transform will extract the transform context information of `orig_keys` then invert the transforms(got from this context information) on the `keys` data. Typical usage is to invert the pre-transforms(applied on input `image`) on the model `pred` data. The output of the inverted data and metadata will be stored at `keys` and `meta_keys` respectively. To correctly invert the transforms, the information of the previously applied transforms should be available at `orig_keys`, and the original metadata at `orig_meta_keys`. (`meta_key_postfix` is an optional string to conveniently construct "meta_keys" and/or "orig_meta_keys".) A detailed usage example is available in the tutorial: https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_inference_dict.py Note: According to the `collate_fn`, this transform may return a list of Tensor without batch dim, thus some following post transforms may not support a list of Tensor, and users can leverage the `post_func` arg for basic processing logic. This transform needs to extract the context information of applied transforms and the meta data dictionary from the input data dictionary, then use some numpy arrays in them to computes the inverse logic, so please don't move `data["{orig_key}_transforms"]` and `data["{orig_meta_key}"]` to GPU device. """ def __init__( self, keys: KeysCollection, transform: InvertibleTransform, loader: TorchDataLoader, orig_keys: KeysCollection, meta_keys: Optional[KeysCollection] = None, orig_meta_keys: Optional[KeysCollection] = None, meta_key_postfix: str = "meta_dict", collate_fn: Optional[Callable] = no_collation, nearest_interp: Union[bool, Sequence[bool]] = True, to_tensor: Union[bool, Sequence[bool]] = True, device: Union[Union[str, torch.device], Sequence[Union[str, torch.device]]] = "cpu", post_func: Union[Callable, Sequence[Callable]] = lambda x: x, num_workers: Optional[int] = 0, allow_missing_keys: bool = False, ) -> None: """ Args: keys: the key of expected data in the dict, invert transforms on it, in-place operation. it also can be a list of keys, will invert transform for each of them, like: ["pred", "pred_class2"]. transform: the previous callable transform that applied on input data. loader: data loader used to run transforms and generate the batch of data. orig_keys: the key of the original input data in the dict. will get the applied transform information for this input data, then invert them for the expected data with `keys`. It can also be a list of keys, each matches to the `keys` data. meta_keys: explicitly indicate the key for the inverted meta data dictionary. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `{key}_{meta_key_postfix}`. orig_meta_keys: the key of the meta data of original input data, will get the `affine`, `data_shape`, etc. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `{orig_key}_{meta_key_postfix}`. meta data will also be inverted and stored in `meta_keys`. meta_key_postfix: if `orig_meta_keys` is None, use `{orig_key}_{meta_key_postfix}` to to fetch the meta data from dict, if `meta_keys` is None, use `{key}_{meta_key_postfix}`. default is `meta_dict`, the meta data is a dictionary object. For example, to handle orig_key `image`, read/write `affine` matrices from the metadata `image_meta_dict` dictionary's `affine` field. the inverted meta dict will be stored with key: "{key}_{meta_key_postfix}". collate_fn: how to collate data after inverse transformations. default won't do any collation, so the output will be a list of PyTorch Tensor or numpy array without batch dim. nearest_interp: whether to use `nearest` interpolation mode when inverting the spatial transforms, default to `True`. If `False`, use the same interpolation mode as the original transform. it also can be a list of bool, each matches to the `keys` data. to_tensor: whether to convert the inverted data into PyTorch Tensor first, default to `True`. it also can be a list of bool, each matches to the `keys` data. device: if converted to Tensor, move the inverted results to target device before `post_func`, default to "cpu", it also can be a list of string or `torch.device`, each matches to the `keys` data. post_func: post processing for the inverted data, should be a callable function. it also can be a list of callable, each matches to the `keys` data. num_workers: number of workers when run data loader for inverse transforms, default to 0 as only run one iteration and multi-processing may be even slower. Set to `None`, to use the `num_workers` of the input transform data loader. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.transform = transform self.inverter = BatchInverseTransform( transform=transform, loader=loader, collate_fn=collate_fn, num_workers=num_workers, ) self.orig_keys = ensure_tuple_rep(orig_keys, len(self.keys)) self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys) if len(self.keys) != len(self.meta_keys): raise ValueError("meta_keys should have the same length as keys.") self.orig_meta_keys = ensure_tuple_rep(orig_meta_keys, len(self.keys)) self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys)) self.nearest_interp = ensure_tuple_rep(nearest_interp, len(self.keys)) self.to_tensor = ensure_tuple_rep(to_tensor, len(self.keys)) self.device = ensure_tuple_rep(device, len(self.keys)) self.post_func = ensure_tuple_rep(post_func, len(self.keys)) self._totensor = ToTensor()
[docs] def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]: d = dict(data) for ( key, orig_key, meta_key, orig_meta_key, meta_key_postfix, nearest_interp, to_tensor, device, post_func, ) in self.key_iterator( d, self.orig_keys, self.meta_keys, self.orig_meta_keys, self.meta_key_postfix, self.nearest_interp, self.to_tensor, self.device, self.post_func, ): transform_key = f"{orig_key}{InverseKeys.KEY_SUFFIX}" if transform_key not in d: warnings.warn(f"transform info of `{orig_key}` is not available or no InvertibleTransform applied.") continue transform_info = d[transform_key] if nearest_interp: transform_info = convert_inverse_interp_mode( trans_info=deepcopy(transform_info), mode="nearest", align_corners=None, ) input = d[key] if isinstance(input, torch.Tensor): input = input.detach() # construct the input dict data for BatchInverseTransform input_dict = { orig_key: input, transform_key: transform_info, } orig_meta_key = orig_meta_key or f"{orig_key}_{meta_key_postfix}" meta_key = meta_key or f"{key}_{meta_key_postfix}" if orig_meta_key in d: input_dict[orig_meta_key] = d[orig_meta_key] with allow_missing_keys_mode(self.transform): # type: ignore inverted = self.inverter(input_dict) # save the inverted data if isinstance(inverted, (tuple, list)): d[key] = [ post_func(self._totensor(i[orig_key]).to(device) if to_tensor else i[orig_key]) for i in inverted ] # save the inverted meta dict if orig_meta_key in d: d[meta_key] = [i.get(orig_meta_key) for i in inverted] else: d[key] = post_func(self._totensor(inverted[orig_key]).to(device) if to_tensor else inverted[orig_key]) # save the inverted meta dict if orig_meta_key in d: d[meta_key] = inverted.get(orig_meta_key) return d
[docs]class SaveClassificationd(MapTransform): """ Save the classification results and meta data into CSV file or other storage. """ def __init__( self, keys: KeysCollection, meta_keys: Optional[KeysCollection] = None, meta_key_postfix: str = "meta_dict", saver: Optional[CSVSaver] = None, output_dir: str = "./", filename: str = "predictions.csv", overwrite: bool = True, flush: bool = True, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to model output, this transform only supports 1 key. See also: :py:class:`monai.transforms.compose.MapTransform` meta_keys: explicitly indicate the key of the corresponding meta data dictionary. for example, for data with key `image`, the metadata by default is in `image_meta_dict`. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `key_{meta_key_postfix}`. will extract the filename of input image to save classifcation results. meta_key_postfix: `key_{postfix}` was used to store the metadata in `LoadImaged`. so need the key to extract the metadata of input image, like filename, etc. default is `meta_dict`. for example, for data with key `image`, the metadata by default is in `image_meta_dict`. the meta data is a dictionary object which contains: filename, original_shape, etc. this arg only works when `meta_keys=None`. if no corresponding metadata, set to `None`. saver: the saver instance to save classification results, if None, create a CSVSaver internally. the saver must provide `save_batch(batch_data, meta_data)` APIs. output_dir: if `saver=None`, specify the directory to save the CSV file. filename: if `saver=None`, specify the name of the saved CSV file. overwrite: if `saver=None`, indicate whether to overwriting existing CSV file content, if True, will clear the file before saving. otherwise, will apend new content to the CSV file. flush: if `saver=None`, indicate whether to write the cache data to CSV file immediately in this transform and clear the cache. default to True. If False, may need user to call `saver.finalize()` manually then. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) if len(self.keys) != 1: raise ValueError("only 1 key is allowed when saving the classification result.") self.saver = saver or CSVSaver(output_dir, filename, overwrite, flush) self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys)) self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))
[docs] def __call__(self, data): d = dict(data) for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix): if meta_key is None and meta_key_postfix is not None: meta_key = f"{key}_{meta_key_postfix}" meta_data = d[meta_key] if meta_key is not None else None self.saver.save_batch(batch_data=d[key], meta_data=meta_data) return d
[docs] def get_saver(self): """ If want to write content into file, may need to call `finalize` of saver when epoch completed. Or users can also get the cache content from `saver` instead of writing into file. """ return self.saver
ActivationsD = ActivationsDict = Activationsd AsDiscreteD = AsDiscreteDict = AsDiscreted KeepLargestConnectedComponentD = KeepLargestConnectedComponentDict = KeepLargestConnectedComponentd LabelToContourD = LabelToContourDict = LabelToContourd MeanEnsembleD = MeanEnsembleDict = MeanEnsembled ProbNMSD = ProbNMSDict = ProbNMSd VoteEnsembleD = VoteEnsembleDict = VoteEnsembled DecollateD = DecollateDict = Decollated InvertD = InvertDict = Invertd SaveClassificationD = SaveClassificationDict = SaveClassificationd