The TumorInvasionAnalysis class - Classifies Tumor progression using RF and predicts on new cases. More...

#include <mitkTumorInvasionClassification.h>

Public Member Functions
	TumorInvasionClassification ()

	~TumorInvasionClassification ()

void	SelectTrainingSamples (DataCollection *collection, unsigned int mode=0)
	SelectTrainingSamples. More...

void	PrepareResponseSamples (DataCollection *collection)
	PrepareResponseSamples. More...

void	LearnProgressionFeatures (DataCollection *collection, std::vector< std::string > modalitiesList, vcl_size_t forestSize=300, vcl_size_t treeDepth=10)
	LearnProgressionFeatures. More...

void	PredictInvasion (DataCollection *collection, std::vector< std::string > modalitiesList)
	PredictGrowth - Classify voxels into remaining healthy / turning into tumor. More...

void	SanitizeResults (DataCollection *collection, std::string resultID="RESULT")
	SanitizeResults - Performs an Opening Operation on tha data to remove isolated misclassifications. More...

void	SetTrainMargin (vcl_size_t dil2d, vcl_size_t dil3d)

void	SetClassRatio (ScalarType ratio)
	SetClassRatio - set ratio of tumor voxels to healthy voxels that is to be used for training. More...

void	SetGlobalsStatsFile (std::string fn)

void	SamplesWeightingActivated (bool isActive)
	SamplesWeightingActivated If activated a weighted mask for the samples is calculated, weighting samples according to their location and ratio. More...

void	SetTargetID (std::string targetID="TARGET")
	SetTargetID sets binary mask denoting future tumor area. More...

void	SetTumorID (std::string tumorID="GTV")
	SetTumorID sets binary mask denoting current tumor area. More...

void	SetMaskID (std::string maskID="BRAINMASK")
	SetMaskID sets binary mask denoting area that is evluated/included in training. More...

void	SetResultID (std::string resultID="RESULT")
	SetResultID sets data item name in which prediction results are stored. More...

void	UseRandomizedTrees (bool useRandom)
	UseRandomizedTrees - use random splits to build up forest. More...

void	DescriptionToLogFile (DataCollection *collection, std::string outputFile)
	DescriptionToLogFile - Append to log file: patient ids and time steps contained in this data collection. More...

void	SaveRandomForest (std::string filename)
	SaveRandomForest - Saves a trained random forest. More...

void	LoadRandomForest (std::string)
	LoadRandomForest - loads a random forest to be used for classification. More...

Detailed Description

The TumorInvasionAnalysis class - Classifies Tumor progression using RF and predicts on new cases.

Provides a way to learn tumor progression from examples, and predict the growth using random forests.

Expected Inputs are DataCollections. The Tumor Segmentations (current extent) are expected to be binary (0 - healthy tissue, 1 - tumor tissue), likewise the target extent that is to be learnt.

Definition at line 41 of file mitkTumorInvasionClassification.h.

Constructor & Destructor Documentation

mitk::TumorInvasionClassification::TumorInvasionClassification ( )

Definition at line 63 of file mitkTumorInvasionClassification.cpp.

mitk::TumorInvasionClassification::~TumorInvasionClassification ( )

inline

Definition at line 45 of file mitkTumorInvasionClassification.h.

Member Function Documentation

void mitk::TumorInvasionClassification::DescriptionToLogFile	(	mitk::DataCollection *	collection,
		std::string	outputFile
	)

DescriptionToLogFile - Append to log file: patient ids and time steps contained in this data collection.

Parameters

collection
outputFile

Definition at line 647 of file mitkTumorInvasionClassification.cpp.

References mitk::DataCollection::GetData(), mitk::DataCollection::GetName(), mitk::DataCollection::IndexToName(), and mitk::DataCollection::Size().

void mitk::TumorInvasionClassification::LearnProgressionFeatures	(	mitk::DataCollection *	collection,
		std::vector< std::string >	modalitiesList,
		vcl_size_t	forestSize = `300`,
		vcl_size_t	treeDepth = `10`
	)

LearnProgressionFeatures.

Parameters

collection	- data collection used for training
modalitiesList	- modalities (data item names from collection) that are to be used for training
forestSize	- number of trees that are trained
treeDepth	- maximal depth for a single tree

Definition at line 582 of file mitkTumorInvasionClassification.cpp.

References mitk::DCUtilities::DC3dDToMatrixXd(), mitk::DCUtilities::DC3dDToMatrixXi(), and MITK_INFO.

Referenced by main().

void mitk::TumorInvasionClassification::LoadRandomForest ( std::string )

LoadRandomForest - loads a random forest to be used for classification.

Parameters

filename

Definition at line 672 of file mitkTumorInvasionClassification.cpp.

References MITK_ERROR.

void mitk::TumorInvasionClassification::PredictInvasion	(	mitk::DataCollection *	collection,
		std::vector< std::string >	modalitiesList
	)

PredictGrowth - Classify voxels into remaining healthy / turning into tumor.

Parameters

collection

Definition at line 614 of file mitkTumorInvasionClassification.cpp.

References mitk::DCUtilities::DC3dDToMatrixXd(), mitk::DCUtilities::MatrixToDC3d(), MITK_ERROR, and MITK_INFO.

Referenced by main().

void mitk::TumorInvasionClassification::PrepareResponseSamples ( mitk::DataCollection * collection )

PrepareResponseSamples.

Selects healthy/tumor tissue samples to be included into training, and weights them to achieve a given ratio. This modifies the target data items in the data collection. Afterwards the target data is encoded as follows: 0 - excluded (e.g. out of brainmask) 1 - no involvement (healthy all the time) 2 - formerly healthy, now tumor (rezivid) 3 - formerly tumor, now necroses (responsive)

Definition at line 496 of file mitkTumorInvasionClassification.cpp.

References mitk::CollectionDilation::DilateBinaryByName(), EnsureDataImageInCollection(), mitk::DataCollectionImageIterator< TDataType, ImageDimension >::GetVoxel(), mitk::DataCollectionImageIterator< TDataType, ImageDimension >::IsAtEnd(), MITK_INFO, mitk::DataCollectionImageIterator< TDataType, ImageDimension >::SetVoxel(), and mitk::DataCollectionImageIterator< TDataType, ImageDimension >::ToBegin().

Referenced by main().

void mitk::TumorInvasionClassification::SamplesWeightingActivated ( bool isActive )

inline

SamplesWeightingActivated If activated a weighted mask for the samples is calculated, weighting samples according to their location and ratio.

Parameters

isActive

Definition at line 127 of file mitkTumorInvasionClassification.h.

Referenced by main().

void mitk::TumorInvasionClassification::SanitizeResults	(	mitk::DataCollection *	collection,
		std::string	resultID = `"RESULT"`
	)

SanitizeResults - Performs an Opening Operation on tha data to remove isolated misclassifications.

RESULTOPEN

Parameters

collection

Definition at line 642 of file mitkTumorInvasionClassification.cpp.

References mitk::CollectionGrayOpening::PerformGrayOpening().

Referenced by main().

void mitk::TumorInvasionClassification::SaveRandomForest ( std::string filename )

SaveRandomForest - Saves a trained random forest.

Parameters

filename

Definition at line 667 of file mitkTumorInvasionClassification.cpp.

References mitk::IOUtil::Save().

Referenced by main().

void mitk::TumorInvasionClassification::SelectTrainingSamples	(	mitk::DataCollection *	collection,
		unsigned int	mode = `0`
	)

SelectTrainingSamples.

Selects healthy/tumor tissue samples to be included into training. This modifies the target data items in the data collection. Afterwards the target data is structured as follows: 0 -voxels excluded from training , 1 - healthy sample , 2 - "tuning-into-tumor" sample

Parameters

collection
erodeTrainingMask	- Performs erosion on target such that border voxels of tumor region will not be included into training

Definition at line 73 of file mitkTumorInvasionClassification.cpp.

References mitk::CollectionDilation::DilateBinaryByName(), EnsureDataImageInCollection(), mitk::CollectionDilation::ErodeBinaryByName(), mitk::DataCollectionImageIterator< TDataType, ImageDimension >::GetVoxel(), mitk::DataCollectionImageIterator< TDataType, ImageDimension >::IsAtEnd(), min(), MITK_INFO, mitk::DataCollectionImageIterator< TDataType, ImageDimension >::SetVoxel(), and mitk::DataCollectionImageIterator< TDataType, ImageDimension >::ToBegin().

Referenced by main().

void mitk::TumorInvasionClassification::SetClassRatio ( ScalarType ratio )

inline

SetClassRatio - set ratio of tumor voxels to healthy voxels that is to be used for training.

perfect ratio cannot allways be achieved, since by default all tumor voxels are included into training (since they ususally are to rare to be skipped)

Parameters

ratio

Definition at line 120 of file mitkTumorInvasionClassification.h.

Referenced by main().

void mitk::TumorInvasionClassification::SetGlobalsStatsFile ( std::string fn )

inline

Definition at line 121 of file mitkTumorInvasionClassification.h.

void mitk::TumorInvasionClassification::SetMaskID ( std::string maskID = "BRAINMASK" )

inline

SetMaskID sets binary mask denoting area that is evluated/included in training.

Parameters

maskID