pyanno4rt.learning_model.features.catalogue

Feature catalogue module.

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The module aims to provide methods and classes to compute and differentiate dosiomic, radiomic and demographic features. It is designed to be an extensible catalogue which holds all available feature definitions.

Overview

Classes

DosiomicFeature	Abstract superclass for dosiomic features.
RadiomicFeature	Abstract superclass for radiomic features.
DemographicFeature	Abstract superclass for demographic features.
DoseMean	Dose mean feature class.
DoseDeviation	Dose deviation feature class.
DoseMaximum	Dose maximum feature class.
DoseMinimum	Dose minimum feature class.
DoseSkewness	Dose skewness feature class.
DoseKurtosis	Dose kurtosis feature class.
DoseEntropy	Dose entropy feature class.
DoseEnergy	Dose energy feature class.
DoseNVoxels	Dose voxel number feature class.
DoseDx	Dose-volume histogram abscissa feature class.
DoseVx	Dose-volume histogram ordinate feature class.
DoseSubvolume	Subvolume dose feature class.
DoseGradient	Dose gradient feature class.
DoseMoment	Dose moment feature class.
SegmentArea	Segment area feature class.
SegmentVolume	Segment volume feature class.
SegmentEigenvalues	Segment eigenvalues feature class.
SegmentEccentricity	Segment eccentricity feature class.
SegmentDensity	Segment density feature class.
SegmentSphericity	Segment sphericity feature class.
SegmentEigenmin	Segment minimum eigenvalue feature class.
SegmentEigenmid	Segment middle eigenvalue feature class.
SegmentEigenmax	Segment maximum eigenvalue feature class.
PatientAge	Patient age feature class.
PatientSex	Patient sex feature class.
PatientDaysafterrt	Patient days-after-radiotherapy feature class.

Classes

class pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Abstract superclass for dosiomic features.

Overview

Methods

compute(dose, *args)	abc Abstract method for computing the feature value.
differentiate(dose, *args)	abc Abstract method for differentiating the feature.

Members

abstract compute(dose, *args)

Abstract method for computing the feature value.

abstract differentiate(dose, *args)

Abstract method for differentiating the feature.

class pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Abstract superclass for radiomic features.

Overview

Methods

compute(mask, spacing)

abc Abstract method for computing the feature value.

Members

abstract compute(mask, spacing)

Abstract method for computing the feature value.

class pyanno4rt.learning_model.features.catalogue.DemographicFeature

Abstract superclass for demographic features.

Overview

Methods

compute(value)

abc Abstract method for computing the feature value.

Members

abstract compute(value)

Abstract method for computing the feature value.

class pyanno4rt.learning_model.features.catalogue.DoseMean

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose mean feature class.

Overview

Methods

function(dose)	static Compute the mean dose.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the mean dose.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseDeviation

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose deviation feature class.

Overview

Methods

function(dose)	static Compute the standard deviation of the dose.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the standard deviation of the dose.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseMaximum

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose maximum feature class.

Overview

Methods

function(dose)	static Compute the maximum dose.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the maximum dose.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseMinimum

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose minimum feature class.

Overview

Methods

function(dose)	static Compute the minimum dose.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the minimum dose.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseSkewness

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose skewness feature class.

Overview

Methods

function(dose)	static Compute the skewness.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the skewness.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseKurtosis

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose kurtosis feature class.

Overview

Methods

function(dose)	static Compute the kurtosis.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the kurtosis.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseEntropy

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose entropy feature class.

Overview

Methods

function(dose)	static Compute the entropy.
gradient(dose)	static Compute the entropy gradient.
compute(dose, *args)	static Call the computation function.
differentiate(dose, *args)	static Call the differentiation function.

Members

static function(dose)

Compute the entropy.

static gradient(dose)

Compute the entropy gradient.

static compute(dose, *args)

Call the computation function.

static differentiate(dose, *args)

Call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseEnergy

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose energy feature class.

Overview

Methods

function(dose)	static Compute the energy.
gradient(dose)	static Compute the energy gradient.
compute(dose, *args)	static Call the computation function.
differentiate(dose, *args)	static Call the differentiation function.

Members

static function(dose)

Compute the energy.

static gradient(dose)

Compute the energy gradient.

static compute(dose, *args)

Call the computation function.

static differentiate(dose, *args)

Call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseNVoxels

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose voxel number feature class.

Overview

Methods

function(dose)	static Compute the number of voxels.
compute(dose, *args)	static Check the jitting status and call the computation function.
differentiate(dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(dose)

Compute the number of voxels.

static compute(dose, *args)

Check the jitting status and call the computation function.

static differentiate(dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseDx

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose-volume histogram abscissa feature class.

Overview

Methods

pyfunction(level, dose)	static Compute the dose-volume histogram abscissa in ‘python’ mode.
matfunction(level, dose)	static Compute the dose-volume histogram abscissa in ‘matlab’ mode.
compute(level, dose, *args)	static Check the jitting status and call the computation function.
differentiate(level, dose, *args)	static Check the jitting status and call the differentiation function.

Members

static pyfunction(level, dose)

Compute the dose-volume histogram abscissa in ‘python’ mode.

static matfunction(level, dose)

Compute the dose-volume histogram abscissa in ‘matlab’ mode.

static compute(level, dose, *args)

Check the jitting status and call the computation function.

static differentiate(level, dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseVx

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose-volume histogram ordinate feature class.

Overview

Methods

function(level, dose)	static Compute the dose-volume histogram ordinate.
compute(level, dose, *args)	static Check the jitting status and call the computation function.
differentiate(level, dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(level, dose)

Compute the dose-volume histogram ordinate.

static compute(level, dose, *args)

Check the jitting status and call the computation function.

static differentiate(level, dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseSubvolume

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Subvolume dose feature class.

Overview

Methods

function(subvolume, _, *args)	static Compute the subvolume dose.
compute(subvolume, dose, *args)	static Check the jitting status and call the computation function.
differentiate(subvolume, dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(subvolume, _, *args)

Compute the subvolume dose.

static compute(subvolume, dose, *args)

Check the jitting status and call the computation function.

static differentiate(subvolume, dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseGradient

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose gradient feature class.

Overview

Methods

function(axis, dose, *args)	static Compute the dose gradient.
compute(axis, dose, *args)	static Check the jitting status and call the computation function.
differentiate(axis, dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(axis, dose, *args)

Compute the dose gradient.

static compute(axis, dose, *args)

Check the jitting status and call the computation function.

static differentiate(axis, dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.DoseMoment

Bases: pyanno4rt.learning_model.features.catalogue.DosiomicFeature

Dose moment feature class.

Overview

Methods

function(coefficients, _, *args)	static Compute the dose moment.
compute(coefficients, dose, *args)	static Check the jitting status and call the computation function.
differentiate(coefficients, dose, *args)	static Check the jitting status and call the differentiation function.

Members

static function(coefficients, _, *args)

Compute the dose moment.

static compute(coefficients, dose, *args)

Check the jitting status and call the computation function.

static differentiate(coefficients, dose, *args)

Check the jitting status and call the differentiation function.

class pyanno4rt.learning_model.features.catalogue.SegmentArea

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment area feature class.

Overview

Methods

compute(mask, spacing)

static Compute the area.

Members

static compute(mask, spacing)

Compute the area.

class pyanno4rt.learning_model.features.catalogue.SegmentVolume

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment volume feature class.

Overview

Methods

compute(mask, spacing)

static Compute the volume.

Members

static compute(mask, spacing)

Compute the volume.

class pyanno4rt.learning_model.features.catalogue.SegmentEigenvalues

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment eigenvalues feature class.

Overview

Methods

compute(mask, spacing)

static Compute all eigenvalues.

Members

static compute(mask, spacing)

Compute all eigenvalues.

class pyanno4rt.learning_model.features.catalogue.SegmentEccentricity

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment eccentricity feature class.

Overview

Methods

compute(mask, spacing)

static Compute the eccentricity.

Members

static compute(mask, spacing)

Compute the eccentricity.

class pyanno4rt.learning_model.features.catalogue.SegmentDensity

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment density feature class.

Overview

Methods

compute(mask, spacing)

static Compute the density.

Members

static compute(mask, spacing)

Compute the density.

class pyanno4rt.learning_model.features.catalogue.SegmentSphericity

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment sphericity feature class.

Overview

Methods

compute(mask, spacing)

static Compute the sphericity.

Members

static compute(mask, spacing)

Compute the sphericity.

class pyanno4rt.learning_model.features.catalogue.SegmentEigenmin

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment minimum eigenvalue feature class.

Overview

Methods

compute(mask, spacing)

static Compute the minimum eigenvalue.

Members

static compute(mask, spacing)

Compute the minimum eigenvalue.

class pyanno4rt.learning_model.features.catalogue.SegmentEigenmid

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment middle eigenvalue feature class.

Overview

Methods

compute(mask, spacing)

static Compute the middle eigenvalue.

Members

static compute(mask, spacing)

Compute the middle eigenvalue.

class pyanno4rt.learning_model.features.catalogue.SegmentEigenmax

Bases: pyanno4rt.learning_model.features.catalogue.RadiomicFeature

Segment maximum eigenvalue feature class.

Overview

Methods

compute(mask, spacing)

static Compute the maximum eigenvalue.

Members

static compute(mask, spacing)

Compute the maximum eigenvalue.

class pyanno4rt.learning_model.features.catalogue.PatientAge

Bases: pyanno4rt.learning_model.features.catalogue.DemographicFeature

Patient age feature class.

Overview

Methods

compute(value)

static Get the age.

Members

static compute(value)

Get the age.

class pyanno4rt.learning_model.features.catalogue.PatientSex

Bases: pyanno4rt.learning_model.features.catalogue.DemographicFeature

Patient sex feature class.

Overview

Methods

compute(value)

static Get the sex.

Members

static compute(value)

Get the sex.

class pyanno4rt.learning_model.features.catalogue.PatientDaysafterrt

Bases: pyanno4rt.learning_model.features.catalogue.DemographicFeature

Patient days-after-radiotherapy feature class.

Overview

Methods

compute(value)

static Get the days-after-radiotherapy.

Members

static compute(value)

Get the days-after-radiotherapy.