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def mean_merge_fn(planes: list):
return np.stack(planes).mean(axis=0)
|
class AssembleInteractionFn():
' Function interface enabling interaction with the `index_expression` and the `data` before it gets added to the\n assembled `prediction` in :class:`.SubjectAssembler`.\n\n\n .. automethod:: __call__\n '
def __call__(self, key, data, index_expr, **kwargs):
'\n\... |
class ApplyTransformInteractionFn(AssembleInteractionFn):
def __init__(self, transform: tfm.Transform) -> None:
self.transform = transform
def __call__(self, key, data, index_expr, **kwargs):
temp = tfm.raise_error_if_entry_not_extracted
tfm.raise_error_entry_not_extracted = False
... |
class PlaneSubjectAssembler(Assembler):
def __init__(self, datasource: extr.PymiaDatasource, merge_fn=mean_merge_fn, zero_fn=numpy_zeros):
"Assembles predictions of one or multiple subjects where predictions are made in all three planes.\n\n This class assembles the prediction from all planes (axi... |
class Subject2dAssembler(Assembler):
def __init__(self, datasource: extr.PymiaDatasource) -> None:
'Assembles predictions of two-dimensional images.\n\n Two-dimensional images do not specifically require assembling. For pipeline compatibility reasons this class provides\n , nevertheless, a ... |
class RandomCrop(tfm.Transform):
def __init__(self, shape: typing.Union[(int, tuple)], axis: typing.Union[(int, tuple)]=None, p: float=1.0, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)):
"Randomly crops the sample to the specified shape.\n\n The sample shape must be bigger than the crop shape.\n\n ... |
class RandomElasticDeformation(tfm.Transform):
def __init__(self, num_control_points: int=4, deformation_sigma: float=5.0, interpolators: tuple=(sitk.sitkBSpline, sitk.sitkNearestNeighbor), spatial_rank: int=2, fill_value: float=0.0, p: float=0.5, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)):
"Randomly tr... |
class RandomMirror(tfm.Transform):
def __init__(self, axis: int=(- 2), p: float=1.0, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)):
"Randomly mirrors the sample along a given axis.\n\n Args:\n p (float): The probability of the mirroring to be applied.\n axis (int): The axis to ... |
class RandomRotation90(tfm.Transform):
def __init__(self, axes: typing.Tuple[int]=((- 3), (- 2)), p: float=1.0, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)):
"Randomly rotates the sample 90, 180, or 270 degrees in the plane specified by axes.\n\n Raises:\n UserWarning: If the plane to ro... |
class RandomShift(tfm.Transform):
def __init__(self, shift: typing.Union[(int, tuple)], axis: typing.Union[(int, tuple)]=None, p: float=1.0, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)):
"Randomly shifts the sample along axes by a value from the interval [-p * size(axis), +p * size(axis)],\n where ... |
class PytorchDatasetAdapter(torch_data.Dataset):
def __init__(self, datasource: extr.PymiaDatasource) -> None:
'A wrapper class for :class:`.PymiaDatasource` to fit the\n `torch.utils.data.Dataset <https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset>`_ interface.\n\n Args:\n ... |
class SubsetSequentialSampler(smplr.Sampler):
def __init__(self, indices):
'Samples elements sequential from a given list of indices, without replacement.\n\n The class adopts the `torch.utils.data.Sampler\n <https://pytorch.org/docs/1.3.0/data.html#torch.utils.data.Sampler>`_ interface.\n\... |
def get_tf_generator(data_source: extr.PymiaDatasource):
'Returns a generator that wraps :class:`.PymiaDatasource` for the TensorFlow data handling.\n\n The returned generator can be used with `tf.data.Dataset.from_generator\n <https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator>`_ in ... |
class ImageProperties():
def __init__(self, image: sitk.Image):
'Represents ITK image properties.\n\n Holds common ITK image meta-data such as the size, origin, spacing, and direction.\n\n See Also:\n SimpleITK provides `itk::simple::Image::CopyInformation`_ to copy image informa... |
class NumpySimpleITKImageBridge():
'A numpy to SimpleITK bridge, which provides static methods to convert between numpy array and SimpleITK image.'
@staticmethod
def convert(array: np.ndarray, properties: ImageProperties) -> sitk.Image:
'Converts a numpy array to a SimpleITK image.\n\n Arg... |
class SimpleITKNumpyImageBridge():
'A SimpleITK to numpy bridge.\n\n Converts SimpleITK images to numpy arrays. Use the ``NumpySimpleITKImageBridge`` to convert back.\n '
@staticmethod
def convert(image: sitk.Image) -> typing.Tuple[(np.ndarray, ImageProperties)]:
'Converts an image to a num... |
class Callback():
'Base class for the interaction with the dataset creation.\n\n Implementations of the :class:`.Callback` class can be provided to :meth:`.Traverser.traverse` in order to\n write/process specific information of the original data.\n '
def on_start(self, params: dict):
'Called... |
class ComposeCallback(Callback):
def __init__(self, callbacks: typing.List[Callback]) -> None:
'Composes many :class:`.Callback` instances and behaves like an single :class:`.Callback` instance.\n\n This class allows passing multiple :class:`.Callback` to :meth:`.Traverser.traverse`.\n\n Ar... |
class MonitoringCallback(Callback):
'Callback that monitors the dataset creation process by logging the progress to the console.'
def on_start(self, params: dict):
print('start dataset creation')
def on_subject(self, params: dict):
index = params[defs.KEY_SUBJECT_INDEX]
subject_f... |
class WriteDataCallback(Callback):
def __init__(self, writer: wr.Writer) -> None:
'Callback that writes the raw data to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write the data.\n '
self.writer = writer
def on_subject(self, params:... |
class WriteEssentialCallback(Callback):
def __init__(self, writer: wr.Writer) -> None:
'Callback that writes the essential information to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write the data.\n '
self.writer = writer
self.re... |
class WriteImageInformationCallback(Callback):
def __init__(self, writer: wr.Writer, category=defs.KEY_IMAGES) -> None:
'Callback that writes the image information (shape, origin, direction, spacing) to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write ... |
class WriteNamesCallback(Callback):
def __init__(self, writer: wr.Writer) -> None:
'Callback that writes the names of the category entries to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write the data.\n '
self.writer = writer
def on... |
class WriteFilesCallback(Callback):
def __init__(self, writer: wr.Writer) -> None:
'Callback that writes the file names to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write the data.\n '
self.writer = writer
self.file_root = None
... |
def get_default_callbacks(writer: wr.Writer, meta_only=False) -> ComposeCallback:
'Provides a selection of commonly used callbacks to write the most important information to the dataset.\n\n Args:\n writer (.creation.writer.Writer): The writer used to write the data.\n meta_only (bool): Whether o... |
class Load(abc.ABC):
'Interface for loading the data during the dataset creation in :meth:`.Traverser.traverse`\n \n .. automethod:: __call__\n '
@abc.abstractmethod
def __call__(self, file_name: str, id_: str, category: str, subject_id: str) -> typing.Tuple[(np.ndarray, typing.Union[(conv.Image... |
class LoadDefault(Load):
'The default loader.\n\n It loads every data item (id/entry, category) for each subject as :code:`sitk.Image`\n and the corresponding :class:`.ImageProperties`.\n '
def __call__(self, file_name: str, id_: str, category: str, subject_id: str) -> typing.Tuple[(np.ndarray, ty... |
def default_concat(data: typing.List[np.ndarray]) -> np.ndarray:
'Default concatenation function used to combine all entries from a category (e.g. T1, T2 data from "images" category)\n in :meth:`.Traverser.traverse`\n\n Args:\n data (list): List of numpy.ndarray entries to be concatenated.\n\n Ret... |
class Traverser():
def __init__(self, categories: typing.Union[(str, typing.Tuple[(str, ...)])]=None):
'Class managing the dataset creation process.\n\n Args:\n categories (str or tuple of str): The categories to traverse. If None, then all categories of a\n :class:`.Subj... |
class Writer(abc.ABC):
'Represents the abstract dataset writer defining an interface for the writing process.'
def __enter__(self):
self.open()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def __del__(self):
self.close()
@abc.abstractm... |
class Hdf5Writer(Writer):
str_type = h5py.special_dtype(vlen=str)
def __init__(self, file_path: str) -> None:
'Writer class for HDF5 file type.\n\n Args:\n file_path(str): The path to the dataset file to write.\n '
self.h5 = None
self.file_path = file_path
... |
def get_writer(file_path: str) -> Writer:
'Get the dataset writer corresponding to the file extension.\n\n Args:\n file_path(str): The path of the dataset file to be written.\n\n Returns:\n .creation.writer.Writer: Writer corresponding to dataset file extension.\n '
... |
def subject_index_to_str(subject_index, nb_subjects):
max_digits = len(str(nb_subjects))
index_str = '{{:0{}}}'.format(max_digits).format(subject_index)
return index_str
|
def convert_to_string(data):
'Converts extracted string data from bytes to string, as strings are handled as bytes since h5py >= 3.0.\n\n The function has been introduced as part of an `issue <https://github.com/rundherum/pymia/issues/40>`_.\n\n Args:\n data: The data to be converted; either :obj:`by... |
class PymiaDatasource():
def __init__(self, dataset_path: str, indexing_strategy: idx.IndexingStrategy=None, extractor: extr.Extractor=None, transform: tfm.Transform=None, subject_subset: list=None, init_reader_once: bool=True) -> None:
'Provides convenient and adaptable reading of the data from a create... |
class Reader(abc.ABC):
def __init__(self, file_path: str) -> None:
'Abstract dataset reader.\n\n Args:\n file_path(str): The path to the dataset file.\n '
super().__init__()
self.file_path = file_path
def __enter__(self):
self.open()
return se... |
class Hdf5Reader(Reader):
'Represents the dataset reader for HDF5 files.'
def __init__(self, file_path: str, category=defs.KEY_IMAGES) -> None:
'Initializes a new instance.\n\n Args:\n file_path(str): The path to the dataset file.\n category(str): The category of an entry... |
def get_reader(file_path: str, direct_open: bool=False) -> Reader:
'Get the dataset reader corresponding to the file extension.\n\n Args:\n file_path(str): The path to the dataset file.\n direct_open(bool): Whether the file should directly be opened.\n\n Returns:\n Reader: Reader corres... |
class SelectionStrategy(abc.ABC):
'Interface for selecting indices according some rule.\n\n .. automethod:: __call__\n .. automethod:: __repr__\n '
@abc.abstractmethod
def __call__(self, sample: dict) -> bool:
'\n\n Args:\n sample (dict): An extracted from :class:`.Pymi... |
class NonConstantSelection(SelectionStrategy):
def __init__(self, loop_axis=None) -> None:
super().__init__()
self.loop_axis = loop_axis
def __call__(self, sample) -> bool:
image_data = sample[defs.KEY_IMAGES]
if (self.loop_axis is None):
return (not self._all_equ... |
class NonBlackSelection(SelectionStrategy):
def __init__(self, black_value: float=0.0) -> None:
self.black_value = black_value
def __call__(self, sample) -> bool:
return (sample[defs.KEY_IMAGES] > self.black_value).any()
def __repr__(self) -> str:
return '{}({})'.format(self.__c... |
class PercentileSelection(SelectionStrategy):
def __init__(self, percentile: float) -> None:
self.percentile = percentile
def __call__(self, sample) -> bool:
image_data = sample[defs.KEY_IMAGES]
percentile_value = np.percentile(image_data, self.percentile)
return (image_data ... |
class WithForegroundSelection(SelectionStrategy):
def __call__(self, sample) -> bool:
return sample[defs.KEY_LABELS].any()
|
class SubjectSelection(SelectionStrategy):
'Select subjects by their name or index.'
def __init__(self, subjects) -> None:
if isinstance(subjects, int):
subjects = (subjects,)
if isinstance(subjects, str):
subjects = (subjects,)
self.subjects = subjects
de... |
class ComposeSelection(SelectionStrategy):
def __init__(self, strategies) -> None:
self.strategies = strategies
def __call__(self, sample) -> bool:
return all((strategy(sample) for strategy in self.strategies))
def __repr__(self) -> str:
return '|'.join((repr(s) for s in self.st... |
def select_indices(data_source: ds.PymiaDatasource, selection_strategy: SelectionStrategy):
selected_indices = []
for (i, sample) in enumerate(data_source):
if selection_strategy(sample):
selected_indices.append(i)
return selected_indices
|
class IndexExpression():
def __init__(self, indexing: t.Union[(int, tuple, t.List[int], t.List[tuple], t.List[list])]=None, axis: t.Union[(int, tuple)]=None) -> None:
'Defines the indexing of a chunk of raw data in the dataset.\n\n Args:\n indexing (int, tuple, list): The indexing. If :... |
class FileCategory():
def __init__(self, entries=None) -> None:
if (entries is None):
entries = {}
self.entries = entries
|
class SubjectFile():
def __init__(self, subject: str, **file_groups) -> None:
'Holds the file information of a subject.\n\n Args:\n subject (str): The subject identifier.\n **file_groups (dict): The groups of file types containing the file path entries.\n '
sel... |
class Transform(abc.ABC):
@abc.abstractmethod
def __call__(self, sample: dict) -> dict:
pass
|
class ComposeTransform(Transform):
def __init__(self, transforms: typing.Iterable[Transform]) -> None:
self.transforms = transforms
def __call__(self, sample: dict) -> dict:
for t in self.transforms:
sample = t(sample)
return sample
|
class LoopEntryTransform(Transform, abc.ABC):
def __init__(self, loop_axis=None, entries=()) -> None:
super().__init__()
self.loop_axis = loop_axis
self.entries = entries
@staticmethod
def loop_entries(sample: dict, fn, entries, loop_axis=None):
for entry in entries:
... |
class IntensityRescale(LoopEntryTransform):
def __init__(self, lower, upper, loop_axis=None, entries=(defs.KEY_IMAGES,)) -> None:
super().__init__(loop_axis=loop_axis, entries=entries)
self.lower = lower
self.upper = upper
def transform_entry(self, np_entry, entry, loop_i=None) -> np... |
class IntensityNormalization(LoopEntryTransform):
def __init__(self, loop_axis=None, entries=(defs.KEY_IMAGES,)) -> None:
super().__init__(loop_axis=loop_axis, entries=entries)
self.normalize_fn = self._normalize
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
... |
class LambdaTransform(LoopEntryTransform):
def __init__(self, lambda_fn, loop_axis=None, entries=(defs.KEY_IMAGES,)) -> None:
super().__init__(loop_axis=loop_axis, entries=entries)
self.lambda_fn = lambda_fn
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
retur... |
class ClipPercentile(LoopEntryTransform):
def __init__(self, upper_percentile: float, lower_percentile: float=None, loop_axis=None, entries=(defs.KEY_IMAGES,)) -> None:
super().__init__(loop_axis=loop_axis, entries=entries)
self.upper_percentile = upper_percentile
if (lower_percentile is ... |
class Relabel(LoopEntryTransform):
def __init__(self, label_changes: typing.Dict[(int, int)], entries=(defs.KEY_LABELS,)) -> None:
super().__init__(loop_axis=None, entries=entries)
self.label_changes = label_changes
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
... |
class Reshape(LoopEntryTransform):
def __init__(self, shapes: dict) -> None:
'Initializes a new instance of the Reshape class.\n\n Args:\n shapes (dict): A dict with keys being the entries and the values the new shapes of the entries.\n E.g. shapes = {defs.KEY_IMAGES: (-1... |
class Permute(LoopEntryTransform):
def __init__(self, permutation: tuple, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)) -> None:
super().__init__(loop_axis=None, entries=entries)
self.permutation = permutation
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
retur... |
class Squeeze(LoopEntryTransform):
def __init__(self, entries=(defs.KEY_IMAGES, defs.KEY_LABELS), squeeze_axis=None) -> None:
super().__init__(loop_axis=None, entries=entries)
self.squeeze_axis = squeeze_axis
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
retu... |
class UnSqueeze(LoopEntryTransform):
def __init__(self, axis=(- 1), entries=(defs.KEY_IMAGES, defs.KEY_LABELS)) -> None:
super().__init__(loop_axis=None, entries=entries)
self.axis = axis
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
return np.expand_dims(np_... |
class SizeCorrection(Transform):
'Size correction transformation.\n\n Corrects the size, i.e. shape, of an array to a given reference shape.\n '
def __init__(self, shape: typing.Tuple[(typing.Union[(None, int)], ...)], pad_value: int=0, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)) -> None:
'Init... |
class Mask(Transform):
def __init__(self, mask_key: str, mask_value: int=0, masking_value: float=0.0, loop_axis=None, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)) -> None:
super().__init__()
self.mask_key = mask_key
self.mask_value = mask_value
self.masking_value = masking_value
... |
class RandomCrop(LoopEntryTransform):
def __init__(self, size: tuple, loop_axis=None, entries=(defs.KEY_IMAGES, defs.KEY_LABELS)) -> None:
super().__init__(loop_axis, entries)
self.size = size
self.slices = None
def transform_entry(self, np_entry, entry, loop_i=None) -> np.ndarray:
... |
def check_and_return(obj, type_):
if (not isinstance(obj, type_)):
raise ValueError("entry must be '{}'".format(type_.__name__))
return obj
|
class Result():
def __init__(self, id_: str, label: str, metric: str, value):
"Represents a result.\n\n Args:\n id_ (str): The identification of the result (e.g., the subject's name).\n label (str): The label of the result (e.g., the foreground).\n metric (str): Th... |
class Evaluator(abc.ABC):
def __init__(self, metrics: typing.List[pymia_metric.Metric]):
'Evaluator base class.\n\n Args:\n metrics (list of pymia_metric.Metric): A list of metrics.\n '
self.metrics = metrics
self.results = []
@abc.abstractmethod
def eval... |
class SegmentationEvaluator(Evaluator):
def __init__(self, metrics: typing.List[pymia_metric.Metric], labels: dict):
'Represents a segmentation evaluator, evaluating metrics on predictions against references.\n\n Args:\n metrics (list of pymia_metric.Metric): A list of metrics.\n ... |
class AreaMetric(SpacingMetric, abc.ABC):
def __init__(self, metric: str='AREA'):
'Represents an area metric base class.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def _calculate_area(self, image: np.ndarray, slice... |
class VolumeMetric(SpacingMetric, abc.ABC):
def __init__(self, metric: str='VOL'):
'Represents a volume metric base class.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def _calculate_volume(self, image: np.ndarray) -... |
class Accuracy(ConfusionMatrixMetric):
def __init__(self, metric: str='ACURCY'):
'Represents an accuracy metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the accuracy.'
... |
class AdjustedRandIndex(ConfusionMatrixMetric):
def __init__(self, metric: str='ADJRIND'):
'Represents an adjusted rand index metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calcula... |
class AreaUnderCurve(ConfusionMatrixMetric):
def __init__(self, metric: str='AUC'):
'Represents an area under the curve metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates th... |
class AverageDistance(SpacingMetric):
def __init__(self, metric: str='AVGDIST'):
'Represents an average (Hausdorff) distance metric.\n\n Calculates the distance between the set of non-zero pixels of two images using the following equation:\n\n .. math:: AVD(A,B) = max(d(A,B), d(B,A)),\n\n ... |
class CohenKappaCoefficient(ConfusionMatrixMetric):
def __init__(self, metric: str='KAPPA'):
"Represents a Cohen's kappa coefficient metric.\n\n Args:\n metric (str): The identification string of the metric.\n "
super().__init__(metric)
def calculate(self):
"... |
class DiceCoefficient(ConfusionMatrixMetric):
def __init__(self, metric: str='DICE'):
'Represents a Dice coefficient metric with empty target handling, defined as:\n\n .. math:: \\begin{cases} 1 & \\left\\vert{y}\\right\\vert = \\left\\vert{\\hat y}\\right\\vert = 0 \\\\ Dice(y,\\hat y) & \\left\\... |
class FalseNegative(ConfusionMatrixMetric):
def __init__(self, metric: str='FN'):
'Represents a false negative metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the false n... |
class FalsePositive(ConfusionMatrixMetric):
def __init__(self, metric: str='FP'):
'Represents a false positive metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the false p... |
class Fallout(ConfusionMatrixMetric):
def __init__(self, metric: str='FALLOUT'):
'Represents a fallout (false positive rate) metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculat... |
class FalseNegativeRate(ConfusionMatrixMetric):
def __init__(self, metric: str='FNR'):
'Represents a false negative rate metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates t... |
class FMeasure(ConfusionMatrixMetric):
def __init__(self, beta: float=1.0, metric: str='FMEASR'):
'Represents a F-measure metric.\n\n Args:\n beta (float): The beta to trade-off precision and recall.\n Use 0.5 or 2 to calculate the F0.5 and F2 measure, respectively.\n ... |
class GlobalConsistencyError(ConfusionMatrixMetric):
def __init__(self, metric: str='GCOERR'):
'Represents a global consistency error metric.\n\n Implementation based on Martin 2001. todo(fabianbalsiger): add entire reference\n\n Args:\n metric (str): The identification string of... |
class HausdorffDistance(DistanceMetric):
def __init__(self, percentile: float=100.0, metric: str='HDRFDST'):
'Represents a Hausdorff distance metric.\n\n Calculates the distance between the set of non-zero pixels of two images using the following equation:\n\n .. math:: H(A,B) = max(h(A,B),... |
class InterclassCorrelation(NumpyArrayMetric):
def __init__(self, metric: str='ICCORR'):
'Represents an interclass correlation metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calcul... |
class JaccardCoefficient(ConfusionMatrixMetric):
def __init__(self, metric: str='JACRD'):
'Represents a Jaccard coefficient metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculate... |
class MahalanobisDistance(NumpyArrayMetric):
def __init__(self, metric: str='MAHLNBS'):
'Represents a Mahalanobis distance metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates... |
class MutualInformation(ConfusionMatrixMetric):
def __init__(self, metric: str='MUTINF'):
'Represents a mutual information metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates... |
class Precision(ConfusionMatrixMetric):
def __init__(self, metric: str='PRCISON'):
'Represents a precision metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the precision.'... |
class PredictionArea(AreaMetric):
def __init__(self, slice_number: int=(- 1), metric: str='PREDAREA'):
'Represents a prediction area metric.\n\n Args:\n slice_number (int): The slice number to calculate the area.\n Defaults to -1, which will calculate the area on the inte... |
class PredictionVolume(VolumeMetric):
def __init__(self, metric: str='PREDVOL'):
'Represents a prediction volume metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the predi... |
class ProbabilisticDistance(NumpyArrayMetric):
def __init__(self, metric: str='PROBDST'):
'Represents a probabilistic distance metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calcul... |
class RandIndex(ConfusionMatrixMetric):
def __init__(self, metric: str='RNDIND'):
'Represents a rand index metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the rand index.... |
class ReferenceArea(AreaMetric):
def __init__(self, slice_number: int=(- 1), metric: str='REFAREA'):
'Represents a reference area metric.\n\n Args:\n slice_number (int): The slice number to calculate the area.\n Defaults to -1, which will calculate the area on the interme... |
class ReferenceVolume(VolumeMetric):
def __init__(self, metric: str='REFVOL'):
'Represents a reference volume metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the referenc... |
class Sensitivity(ConfusionMatrixMetric):
def __init__(self, metric: str='SNSVTY'):
'Represents a sensitivity (true positive rate or recall) metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
... |
class Specificity(ConfusionMatrixMetric):
def __init__(self, metric: str='SPCFTY'):
'Represents a specificity metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the specific... |
class SurfaceDiceOverlap(DistanceMetric):
def __init__(self, tolerance: float=1, metric: str='SURFDICE'):
'Represents a surface Dice coefficient overlap metric.\n\n Args:\n tolerance (float): The tolerance of the surface distance in mm.\n metric (str): The identification stri... |
class SurfaceOverlap(DistanceMetric):
def __init__(self, tolerance: float=1.0, prediction_to_reference: bool=True, metric: str='SURFOVLP'):
'Represents a surface overlap metric.\n\n Computes the overlap of the reference surface with the predicted surface and vice versa allowing a\n specifie... |
class TrueNegative(ConfusionMatrixMetric):
def __init__(self, metric: str='TN'):
'Represents a true negative metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the true nega... |
class TruePositive(ConfusionMatrixMetric):
def __init__(self, metric: str='TP'):
'Represents a true positive metric.\n\n Args:\n metric (str): The identification string of the metric.\n '
super().__init__(metric)
def calculate(self):
'Calculates the true posi... |
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