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Atropos.cxx
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Atropos.cxx
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#include <string>
#include <algorithm>
#include <vector>
#include <algorithm>
#include "antsUtilities.h"
#include "ReadWriteData.h"
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImageRegionIterator.h"
#include "itkMaskImageFilter.h"
#include "itkNumericSeriesFileNames.h"
#include "itkSymmetricSecondRankTensor.h"
#include "itkVectorImage.h"
#include "itkVectorIndexSelectionCastImageFilter.h"
#include "antsAtroposSegmentationImageFilter.h"
#include "antsBoxPlotQuantileListSampleFilter.h"
#include "antsCommandLineOption.h"
#include "antsCommandLineParser.h"
#include "antsGaussianListSampleFunction.h"
#include "antsLogEuclideanGaussianListSampleFunction.h"
#include "antsGrubbsRosnerListSampleFilter.h"
#include "antsHistogramParzenWindowsListSampleFunction.h"
#include "antsJointHistogramParzenShapeAndOrientationListSampleFunction.h"
#include "antsListSampleToListSampleFilter.h"
#include "antsManifoldParzenWindowsListSampleFunction.h"
#include "antsPassThroughListSampleFilter.h"
#include "antsPartialVolumeGaussianListSampleFunction.h"
#include "itkTimeProbe.h"
namespace ants
{
template <typename TFilter>
class CommandIterationUpdate final : public itk::Command
{
public:
using Self = CommandIterationUpdate<TFilter>;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
const auto * filter = dynamic_cast<const TFilter *>(object);
if (typeid(event) != typeid(itk::IterationEvent))
{
return;
}
std::cout << " Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations()
<< "): ";
std::cout << "posterior probability = " << filter->GetCurrentPosteriorProbability();
using RealType = typename TFilter::RealType;
RealType annealingTemperature =
filter->GetInitialAnnealingTemperature() *
std::pow(filter->GetAnnealingRate(), static_cast<RealType>(filter->GetElapsedIterations()));
annealingTemperature = std::max(annealingTemperature, filter->GetMinimumAnnealingTemperature());
std::cout << " (annealing temperature = " << annealingTemperature << ")" << std::endl;
}
};
template <unsigned int ImageDimension>
int
AtroposSegmentation(itk::ants::CommandLineParser * parser)
{
using PixelType = float;
using RealType = float;
using InputImageType = itk::Image<PixelType, ImageDimension>;
using LabelType = unsigned int;
using LabelImageType = itk::Image<LabelType, ImageDimension>;
bool verbose = false;
typename itk::ants::CommandLineParser::OptionType::Pointer verboseOption = parser->GetOption("verbose");
if (verboseOption && verboseOption->GetNumberOfFunctions())
{
verbose = parser->Convert<bool>(verboseOption->GetFunction(0)->GetName());
}
if (verbose)
{
std::cout << std::endl << "Running Atropos for " << ImageDimension << "-dimensional images." << std::endl;
}
using SegmentationFilterType = itk::ants::AtroposSegmentationImageFilter<InputImageType, LabelImageType>;
typename SegmentationFilterType::Pointer segmenter = SegmentationFilterType::New();
if (verbose)
{
using CommandType = CommandIterationUpdate<SegmentationFilterType>;
typename CommandType::Pointer observer = CommandType::New();
segmenter->AddObserver(itk::IterationEvent(), observer);
}
/**
* memory-usage -- need to set before setting the prior probability images.
*/
typename itk::ants::CommandLineParser::OptionType::Pointer memoryOption = parser->GetOption("minimize-memory-usage");
if (memoryOption && memoryOption->GetNumberOfFunctions())
{
segmenter->SetMinimizeMemoryUsage(parser->Convert<bool>(memoryOption->GetFunction(0)->GetName()));
}
/**
* Initialization
*/
typename itk::ants::CommandLineParser::OptionType::Pointer initializationOption = parser->GetOption("initialization");
if (!initializationOption || !initializationOption->GetNumberOfFunctions())
{
std::cerr << "No initialization option specified." << std::endl;
return EXIT_FAILURE;
}
if (initializationOption && initializationOption->GetNumberOfFunctions() &&
initializationOption->GetFunction(0)->GetNumberOfParameters() < 1)
{
if (verbose)
{
std::cerr << "Incorrect initialization option specification." << std::endl;
std::cerr << " " << initializationOption->GetDescription() << std::endl;
}
return EXIT_FAILURE;
}
else
{
segmenter->SetNumberOfTissueClasses(
parser->Convert<unsigned int>(initializationOption->GetFunction(0)->GetParameter(0)));
std::string initializationStrategy = initializationOption->GetFunction(0)->GetName();
ConvertToLowerCase(initializationStrategy);
if (!initializationStrategy.compare(std::string("random")))
{
segmenter->SetInitializationStrategy(SegmentationFilterType::Random);
}
else if (!initializationStrategy.compare(std::string("otsu")))
{
segmenter->SetInitializationStrategy(SegmentationFilterType::Otsu);
}
else if (!initializationStrategy.compare(std::string("kmeans")))
{
segmenter->SetInitializationStrategy(SegmentationFilterType::KMeans);
if (initializationOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
std::vector<float> clusterCenters =
parser->ConvertVector<float>(initializationOption->GetFunction(0)->GetParameter(1));
if (clusterCenters.size() != segmenter->GetNumberOfTissueClasses())
{
if (verbose)
{
std::cerr << "The cluster center vector size does not equal the "
<< "specified number of classes." << std::endl;
}
return EXIT_FAILURE;
}
else
{
typename SegmentationFilterType::ParametersType parameters;
parameters.SetSize(segmenter->GetNumberOfTissueClasses());
for (unsigned int n = 0; n < parameters.GetSize(); n++)
{
parameters[n] = clusterCenters[n];
}
segmenter->SetInitialKMeansParameters(parameters);
}
}
}
else if (!initializationStrategy.compare(std::string("priorprobabilityimages")))
{
segmenter->SetInitializationStrategy(SegmentationFilterType::PriorProbabilityImages);
if (initializationOption->GetFunction(0)->GetNumberOfParameters() < 3)
{
if (verbose)
{
std::cerr << "Incorrect initialization option specification." << std::endl;
std::cerr << " " << initializationOption->GetDescription() << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetPriorProbabilityWeight(
parser->Convert<float>(initializationOption->GetFunction(0)->GetParameter(2)));
if (initializationOption->GetFunction(0)->GetNumberOfParameters() > 3)
{
segmenter->SetProbabilityThreshold(
parser->Convert<float>(initializationOption->GetFunction(0)->GetParameter(3)));
}
std::string filename = initializationOption->GetFunction(0)->GetParameter(1);
if (filename.find(std::string("%")) != std::string::npos)
{
itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New();
fileNamesCreator->SetStartIndex(1);
fileNamesCreator->SetEndIndex(segmenter->GetNumberOfTissueClasses());
fileNamesCreator->SetSeriesFormat(filename.c_str());
const std::vector<std::string> & imageNames = fileNamesCreator->GetFileNames();
for (unsigned int k = 0; k < imageNames.size(); k++)
{
typename InputImageType::Pointer image;
if (!ReadImage<InputImageType>(image, imageNames[k].c_str()))
{
std::cout << "Input prior probability image could not be read: " << imageNames[k] << std::endl;
return EXIT_FAILURE;
}
segmenter->SetPriorProbabilityImage(k + 1, image);
}
}
else
{
using VectorImageType = itk::VectorImage<PixelType, ImageDimension>;
typename VectorImageType::Pointer image;
if (!ReadImage<VectorImageType>(image, filename.c_str()))
{
std::cout << "Input prior probability image could not be read." << std::endl;
return EXIT_FAILURE;
}
if (image->GetNumberOfComponentsPerPixel() != segmenter->GetNumberOfTissueClasses())
{
if (verbose)
{
std::cerr << "The number of components does not match the number of "
<< "classes." << std::endl;
}
return EXIT_FAILURE;
}
using CasterType = itk::VectorIndexSelectionCastImageFilter<VectorImageType, InputImageType>;
typename CasterType::Pointer caster = CasterType::New();
caster->SetInput(image);
for (unsigned int k = 0; k < segmenter->GetNumberOfTissueClasses(); k++)
{
caster->SetIndex(k);
caster->Update();
segmenter->SetPriorProbabilityImage(k + 1, caster->GetOutput());
}
}
if (initializationOption->GetFunction(0)->GetNumberOfParameters() > 3)
{
segmenter->SetProbabilityThreshold(
parser->Convert<float>(initializationOption->GetFunction(0)->GetParameter(3)));
}
}
else if (!initializationStrategy.compare(std::string("priorlabelimage")))
{
segmenter->SetInitializationStrategy(SegmentationFilterType::PriorLabelImage);
if (initializationOption->GetFunction(0)->GetNumberOfParameters() < 3)
{
if (verbose)
{
std::cerr << "Incorrect initialization option specification." << std::endl;
std::cerr << " " << initializationOption->GetDescription() << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetPriorProbabilityWeight(
parser->Convert<float>(initializationOption->GetFunction(0)->GetParameter(2)));
std::string filename = initializationOption->GetFunction(0)->GetParameter(1);
typename LabelImageType::Pointer image;
if (!ReadImage<LabelImageType>(image, filename.c_str()))
{
std::cout << "Input prior label image could not be read." << std::endl;
return EXIT_FAILURE;
}
segmenter->SetPriorLabelImage(image);
}
else
{
if (verbose)
{
std::cerr << "Unrecognized initialization strategy request." << std::endl;
}
return EXIT_FAILURE;
}
}
/**
* Posterior probability formulation
*/
typename itk::ants::CommandLineParser::OptionType::Pointer posteriorOption =
parser->GetOption("posterior-formulation");
if (posteriorOption && posteriorOption->GetNumberOfFunctions())
{
if (posteriorOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
segmenter->SetUseMixtureModelProportions(parser->Convert<bool>(posteriorOption->GetFunction(0)->GetParameter(0)));
RealType annealingTemperature = 1.0;
if (posteriorOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
annealingTemperature = parser->Convert<RealType>(posteriorOption->GetFunction(0)->GetParameter(1));
if (annealingTemperature <= itk::NumericTraits<RealType>::ZeroValue())
{
if (verbose)
{
std::cerr << "Annealing temperature must be positive." << std::endl;
}
return EXIT_FAILURE;
}
}
segmenter->SetInitialAnnealingTemperature(annealingTemperature);
RealType annealingRate = 1.0;
if (posteriorOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
annealingRate = parser->Convert<RealType>(posteriorOption->GetFunction(0)->GetParameter(2));
if (annealingRate < itk::NumericTraits<RealType>::ZeroValue() ||
annealingRate > itk::NumericTraits<RealType>::OneValue())
{
if (verbose)
{
std::cerr << "Annealing rate must be in the range [0, 1]." << std::endl;
}
return EXIT_FAILURE;
}
}
segmenter->SetAnnealingRate(annealingRate);
if (posteriorOption->GetFunction(0)->GetNumberOfParameters() > 3)
{
auto minimumAnnealingTemperature = parser->Convert<RealType>(posteriorOption->GetFunction(0)->GetParameter(3));
segmenter->SetMinimumAnnealingTemperature(minimumAnnealingTemperature);
}
}
std::string posteriorStrategy = posteriorOption->GetFunction(0)->GetName();
ConvertToLowerCase(posteriorStrategy);
if (!posteriorStrategy.compare(std::string("socrates")))
{
segmenter->SetPosteriorProbabilityFormulation(SegmentationFilterType::Socrates);
}
else if (!posteriorStrategy.compare(std::string("plato")))
{
segmenter->SetPosteriorProbabilityFormulation(SegmentationFilterType::Plato);
}
else if (!posteriorStrategy.compare(std::string("aristotle")))
{
segmenter->SetPosteriorProbabilityFormulation(SegmentationFilterType::Aristotle);
}
else if (!posteriorStrategy.compare(std::string("sigmoid")))
{
segmenter->SetPosteriorProbabilityFormulation(SegmentationFilterType::Sigmoid);
}
}
/**
* convergence options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer convergenceOption = parser->GetOption("convergence");
if (convergenceOption && convergenceOption->GetNumberOfFunctions())
{
if (convergenceOption->GetFunction(0)->GetNumberOfParameters() == 0)
{
segmenter->SetMaximumNumberOfIterations(
parser->Convert<unsigned int>(convergenceOption->GetFunction(0)->GetName()));
}
if (convergenceOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
segmenter->SetMaximumNumberOfIterations(
parser->Convert<unsigned int>(convergenceOption->GetFunction(0)->GetParameter(0)));
}
if (convergenceOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
segmenter->SetConvergenceThreshold(parser->Convert<float>(convergenceOption->GetFunction(0)->GetParameter(1)));
}
}
/**
* mask image
*/
typename itk::ants::CommandLineParser::OptionType::Pointer maskOption = parser->GetOption("mask-image");
if (maskOption && maskOption->GetNumberOfFunctions())
{
try
{
typename LabelImageType::Pointer image;
if (!ReadImage<LabelImageType>(image, maskOption->GetFunction(0)->GetName().c_str()))
{
std::cout << "Input mask image could not be read." << std::endl;
return EXIT_FAILURE;
}
segmenter->SetMaskImage(image);
// Check to see that the labels in the prior label image or the non-zero
// probability voxels in the prior probability images encompass the entire
// mask region.
if (segmenter->GetInitializationStrategy() == SegmentationFilterType::PriorLabelImage)
{
itk::ImageRegionConstIterator<LabelImageType> ItM(segmenter->GetMaskImage(),
segmenter->GetMaskImage()->GetLargestPossibleRegion());
itk::ImageRegionConstIterator<LabelImageType> ItP(segmenter->GetPriorLabelImage(),
segmenter->GetPriorLabelImage()->GetLargestPossibleRegion());
for (ItM.GoToBegin(), ItP.GoToBegin(); !ItM.IsAtEnd(); ++ItM, ++ItP)
{
if (ItM.Get() != itk::NumericTraits<LabelType>::ZeroValue() && ItP.Get() == 0)
{
if (verbose)
{
std::cout << std::endl;
std::cout << "Warning: the labels in the the prior label image do "
<< "not encompass the entire mask region. As a result each unlabeled voxel will be "
<< "initially assigned a random label. The user might want to consider "
<< "various alternative strategies like assigning an additional "
<< "\"background\" label to the unlabeled voxels or propagating "
<< "the labels within the mask region." << std::endl;
std::cout << std::endl;
}
break;
}
}
}
else if (segmenter->GetInitializationStrategy() == SegmentationFilterType::PriorProbabilityImages)
{
itk::ImageRegionConstIteratorWithIndex<LabelImageType> ItM(
segmenter->GetMaskImage(), segmenter->GetMaskImage()->GetLargestPossibleRegion());
for (ItM.GoToBegin(); !ItM.IsAtEnd(); ++ItM)
{
if (ItM.Get() != itk::NumericTraits<LabelType>::ZeroValue())
{
RealType sumPriorProbability = 0.0;
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
sumPriorProbability += segmenter->GetPriorProbabilityImage(n + 1)->GetPixel(ItM.GetIndex());
}
if (sumPriorProbability < segmenter->GetProbabilityThreshold())
{
if (verbose)
{
std::cout << std::endl;
std::cout << "Warning: the sum of the priors from the the prior probability images are "
<< "less than the probability threshold within the mask region. As a result "
<< "each zero probability voxel will be "
<< "initially assigned a random label. The user might want to consider "
<< "various alternative strategies like assigning an additional "
<< "\"background\" label to the zero probability voxels or propagating "
<< "the probabilities within the mask region." << std::endl;
std::cout << std::endl;
}
break;
}
}
}
}
}
catch (...)
{}
}
else
{
if (verbose)
{
std::cerr << "An image mask is required. Specify a mask image"
<< " with the -x option." << std::endl;
}
return EXIT_FAILURE;
}
/**
* BSpline options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer bsplineOption = parser->GetOption("bspline");
if (bsplineOption && bsplineOption->GetNumberOfFunctions())
{
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
std::vector<unsigned int> numLevels =
parser->ConvertVector<unsigned int>(bsplineOption->GetFunction(0)->GetParameter(0));
typename SegmentationFilterType::ArrayType numberOfFittingLevels;
if (numLevels.size() == 1)
{
numberOfFittingLevels.Fill(numLevels[0]);
}
else if (numLevels.size() == ImageDimension)
{
for (unsigned int d = 0; d < ImageDimension; d++)
{
numberOfFittingLevels[d] = numLevels[d];
}
}
else
{
if (verbose)
{
std::cerr << "Incorrect number of levels" << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetNumberOfLevels(numberOfFittingLevels);
}
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
segmenter->SetSplineOrder(parser->Convert<unsigned int>(bsplineOption->GetFunction(0)->GetParameter(2)));
}
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
std::vector<unsigned int> array =
parser->ConvertVector<unsigned int>(bsplineOption->GetFunction(0)->GetParameter(1));
typename SegmentationFilterType::ArrayType numberOfControlPoints;
if (array.size() == 1)
{
numberOfControlPoints.Fill(array[0] + segmenter->GetSplineOrder());
}
else if (array.size() == ImageDimension)
{
for (unsigned int d = 0; d < ImageDimension; d++)
{
numberOfControlPoints[d] = array[d] + segmenter->GetSplineOrder();
}
}
else
{
if (verbose)
{
std::cerr << "Incorrect mesh resolution" << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetNumberOfControlPoints(numberOfControlPoints);
}
}
/**
* labels
*/
typename itk::ants::CommandLineParser::OptionType::Pointer labelOption = parser->GetOption("label-propagation");
if (labelOption && labelOption->GetNumberOfFunctions())
{
if (labelOption->GetNumberOfFunctions() == 1 && (labelOption->GetFunction(0)->GetName()).empty())
{
typename SegmentationFilterType::LabelParameterMapType labelMap;
auto labelLambda = parser->Convert<float>(labelOption->GetFunction(0)->GetParameter(0));
float labelBoundaryProbability = 1.0;
if (labelOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
labelBoundaryProbability = parser->Convert<float>(labelOption->GetFunction(0)->GetParameter(1));
if (labelBoundaryProbability < itk::NumericTraits<float>::ZeroValue())
{
labelBoundaryProbability = itk::NumericTraits<float>::ZeroValue();
}
if (labelBoundaryProbability > itk::NumericTraits<float>::OneValue())
{
labelBoundaryProbability = itk::NumericTraits<float>::OneValue();
}
}
for (unsigned int n = 1; n <= segmenter->GetNumberOfTissueClasses(); n++)
{
typename SegmentationFilterType::LabelParametersType labelPair;
labelPair.first = labelLambda;
labelPair.second = labelBoundaryProbability;
labelMap[n] = labelPair;
}
segmenter->SetPriorLabelParameterMap(labelMap);
}
else
{
typename SegmentationFilterType::LabelParameterMapType labelMap;
for (unsigned int n = 0; n < labelOption->GetNumberOfFunctions(); n++)
{
typename SegmentationFilterType::LabelParametersType labelPair;
auto labelLambda = parser->Convert<float>(labelOption->GetFunction(n)->GetParameter(0));
float labelBoundaryProbability = 1.0;
if (labelOption->GetFunction(n)->GetNumberOfParameters() > 1)
{
labelBoundaryProbability = parser->Convert<float>(labelOption->GetFunction(n)->GetParameter(1));
if (labelBoundaryProbability < itk::NumericTraits<float>::ZeroValue())
{
labelBoundaryProbability = itk::NumericTraits<float>::ZeroValue();
}
if (labelBoundaryProbability > itk::NumericTraits<float>::OneValue())
{
labelBoundaryProbability = itk::NumericTraits<float>::OneValue();
}
}
labelPair.first = labelLambda;
labelPair.second = labelBoundaryProbability;
auto whichClass = parser->Convert<unsigned int>(labelOption->GetFunction(n)->GetName());
labelMap[whichClass] = labelPair;
}
segmenter->SetPriorLabelParameterMap(labelMap);
}
}
/**
* intensity images
*/
typename itk::ants::CommandLineParser::OptionType::Pointer imageOption = parser->GetOption("intensity-image");
if (imageOption && imageOption->GetNumberOfFunctions())
{
unsigned int count = 0;
for (int n = imageOption->GetNumberOfFunctions() - 1; n >= 0; n--)
{
std::string imagename;
if (imageOption->GetFunction(n)->GetNumberOfParameters() > 0)
{
imagename = imageOption->GetFunction(n)->GetParameter(0);
}
else
{
imagename = imageOption->GetFunction(n)->GetName();
}
typename InputImageType::Pointer image;
if (!ReadImage<InputImageType>(image, imagename.c_str()))
{
std::cout << "Input intensity image could not be read." << std::endl;
return EXIT_FAILURE;
}
segmenter->SetIntensityImage(count, image);
if (imageOption->GetFunction(count)->GetNumberOfParameters() > 1)
{
segmenter->SetAdaptiveSmoothingWeight(count,
parser->Convert<float>(imageOption->GetFunction(count)->GetParameter(1)));
}
else
{
segmenter->SetAdaptiveSmoothingWeight(count, 0.0);
}
count++;
}
}
else
{
if (verbose)
{
std::cerr << "No input images were specified. Specify an input image"
<< " with the -a option." << std::endl;
}
return EXIT_FAILURE;
}
/**
* MRF options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer mrfOption = parser->GetOption("mrf");
if (mrfOption && mrfOption->GetNumberOfFunctions())
{
if (mrfOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
try
{
using MRFCoefficientImageType = typename SegmentationFilterType::RealImageType;
using MRFNeighborhoodImageReaderType = itk::ImageFileReader<MRFCoefficientImageType>;
typename MRFNeighborhoodImageReaderType::Pointer mrfNeighborhoodReader = MRFNeighborhoodImageReaderType::New();
mrfNeighborhoodReader->SetFileName(mrfOption->GetFunction(0)->GetParameter(0));
typename MRFCoefficientImageType::Pointer mrfCoefficientImage = mrfNeighborhoodReader->GetOutput();
mrfCoefficientImage->Update();
mrfCoefficientImage->DisconnectPipeline();
segmenter->SetMRFCoefficientImage(mrfCoefficientImage);
}
catch (...)
{
segmenter->SetMRFSmoothingFactor(parser->Convert<float>(mrfOption->GetFunction(0)->GetParameter(0)));
}
}
if (mrfOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
std::vector<unsigned int> array = parser->ConvertVector<unsigned int>(mrfOption->GetFunction(0)->GetParameter(1));
typename SegmentationFilterType::ArrayType radius;
if (array.size() == 1)
{
radius.Fill(array[0]);
}
else if (array.size() == ImageDimension)
{
for (unsigned int d = 0; d < ImageDimension; d++)
{
radius[d] = array[d];
}
}
else
{
if (verbose)
{
std::cerr << "MRF radius size needs to be equal to the image dimension." << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetMRFRadius(radius);
}
}
/**
* ICM options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer icmOption = parser->GetOption("icm");
if (icmOption && icmOption->GetNumberOfFunctions() == 1)
{
segmenter->SetUseAsynchronousUpdating(parser->Convert<bool>(icmOption->GetFunction(0)->GetName()));
}
if (icmOption && icmOption->GetNumberOfFunctions())
{
if (icmOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
segmenter->SetUseAsynchronousUpdating(parser->Convert<bool>(icmOption->GetFunction(0)->GetParameter(0)));
}
if (icmOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
segmenter->SetMaximumNumberOfICMIterations(
parser->Convert<unsigned int>(icmOption->GetFunction(0)->GetParameter(1)));
}
}
/**
* random seed
*/
typename itk::ants::CommandLineParser::OptionType::Pointer seedOption = parser->GetOption("use-random-seed");
if (seedOption && seedOption->GetNumberOfFunctions())
{
bool useRandomSeed = parser->Convert<bool>(seedOption->GetFunction(0)->GetName());
if (!useRandomSeed)
{
// assign seed from itkMersenneTwisterRandomVariateGenerator.h (line 347)
segmenter->SetRandomizerInitializationSeed(19650218UL);
}
}
/**
* euclidean distance
*/
typename itk::ants::CommandLineParser::OptionType::Pointer distanceOption =
parser->GetOption("use-euclidean-distance");
if (distanceOption && distanceOption->GetNumberOfFunctions())
{
segmenter->SetUseEuclideanDistanceForPriorLabels(parser->Convert<bool>(distanceOption->GetFunction(0)->GetName()));
}
/**
* likelihood
*/
typename itk::ants::CommandLineParser::OptionType::Pointer likelihoodOption = parser->GetOption("likelihood-model");
if (likelihoodOption && likelihoodOption->GetNumberOfFunctions())
{
std::string likelihoodModel = likelihoodOption->GetFunction(0)->GetName();
ConvertToLowerCase(likelihoodModel);
if (!likelihoodModel.compare(std::string("gaussian")))
{
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType = itk::ants::Statistics::GaussianListSampleFunction<SampleType, float, float>;
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
typename LikelihoodType::Pointer gaussianLikelihood = LikelihoodType::New();
segmenter->SetLikelihoodFunction(n, gaussianLikelihood);
}
}
else if (!likelihoodModel.compare(std::string("manifoldparzenwindows")))
{
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType = itk::ants::Statistics::ManifoldParzenWindowsListSampleFunction<SampleType, float, float>;
float regularizationSigma = 1.0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
regularizationSigma = parser->Convert<float>(likelihoodOption->GetFunction(0)->GetParameter(0));
}
unsigned int evalNeighborhood = 50;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
evalNeighborhood = parser->Convert<unsigned int>(likelihoodOption->GetFunction(0)->GetParameter(1));
}
unsigned int covNeighborhood = 0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
covNeighborhood = parser->Convert<unsigned int>(likelihoodOption->GetFunction(0)->GetParameter(2));
}
float covSigma = 1.0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 3)
{
covSigma = parser->Convert<float>(likelihoodOption->GetFunction(0)->GetParameter(3));
}
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
typename LikelihoodType::Pointer mpwLikelihood = LikelihoodType::New();
mpwLikelihood->SetRegularizationSigma(regularizationSigma);
mpwLikelihood->SetEvaluationKNeighborhood(evalNeighborhood);
mpwLikelihood->SetCovarianceKNeighborhood(covNeighborhood);
mpwLikelihood->SetKernelSigma(covSigma);
segmenter->SetLikelihoodFunction(n, mpwLikelihood);
}
}
else if (!likelihoodModel.compare(std::string("histogramparzenwindows")))
{
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType = itk::ants::Statistics::HistogramParzenWindowsListSampleFunction<SampleType, float, float>;
float sigma = 1.0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
sigma = parser->Convert<float>(likelihoodOption->GetFunction(0)->GetParameter(0));
}
unsigned int numberOfBins = 32;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
numberOfBins = parser->Convert<unsigned int>(likelihoodOption->GetFunction(0)->GetParameter(1));
}
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
typename LikelihoodType::Pointer hpwLikelihood = LikelihoodType::New();
hpwLikelihood->SetSigma(sigma);
hpwLikelihood->SetNumberOfHistogramBins(numberOfBins);
segmenter->SetLikelihoodFunction(n, hpwLikelihood);
}
}
else if (!likelihoodModel.compare(std::string("jointshapeandorientationprobability")))
{
if (segmenter->GetNumberOfIntensityImages() !=
static_cast<unsigned int>(ImageDimension * (ImageDimension + 1) / 2))
{
if (verbose)
{
std::cerr << " Expect images in upper triangular order " << std::endl;
std::cerr << " xx xy xz yy yz zz " << std::endl;
std::cerr << "Incorrect number of intensity images specified." << std::endl;
}
return EXIT_FAILURE;
}
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType =
itk::ants::Statistics::JointHistogramParzenShapeAndOrientationListSampleFunction<SampleType, float, float>;
float shapeSigma = 2.0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
shapeSigma = parser->Convert<float>(likelihoodOption->GetFunction(0)->GetParameter(0));
}
unsigned int numberOfShapeBins = 64;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
numberOfShapeBins = parser->Convert<unsigned int>(likelihoodOption->GetFunction(0)->GetParameter(1));
}
float orientationSigma = 1.0;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
orientationSigma = parser->Convert<float>(likelihoodOption->GetFunction(0)->GetParameter(2));
}
unsigned int numberOfOrientationBins = 32;
if (likelihoodOption->GetFunction(0)->GetNumberOfParameters() > 3)
{
numberOfOrientationBins = parser->Convert<unsigned int>(likelihoodOption->GetFunction(0)->GetParameter(3));
}
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
typename LikelihoodType::Pointer hpwLikelihood = LikelihoodType::New();
hpwLikelihood->SetShapeSigma(shapeSigma);
hpwLikelihood->SetOrientationSigma(orientationSigma);
hpwLikelihood->SetNumberOfShapeJointHistogramBins(numberOfShapeBins);
hpwLikelihood->SetNumberOfOrientationJointHistogramBins(numberOfOrientationBins);
segmenter->SetLikelihoodFunction(n, hpwLikelihood);
}
}
else if (!likelihoodModel.compare(std::string("logeuclideangaussian")))
{
if (segmenter->GetNumberOfIntensityImages() !=
static_cast<unsigned int>(ImageDimension * (ImageDimension + 1) / 2))
{
if (verbose)
{
std::cerr << " Expect images in upper triangular order " << std::endl;
std::cerr << " xx xy xz yy yz zz " << std::endl;
std::cerr << "Incorrect number of intensity images specified." << std::endl;
}
return EXIT_FAILURE;
}
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType = itk::ants::Statistics::LogEuclideanGaussianListSampleFunction<SampleType, float, float>;
for (unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++)
{
typename LikelihoodType::Pointer gaussianLikelihood = LikelihoodType::New();
segmenter->SetLikelihoodFunction(n, gaussianLikelihood);
}
}
else
{
if (verbose)
{
std::cerr << "Unrecognized likelihood model request." << std::endl;
}
return EXIT_FAILURE;
}
}
/**
* partial volume
*/
typename itk::ants::CommandLineParser::OptionType::Pointer pvOption = parser->GetOption("partial-volume-label-set");
if (pvOption && pvOption->GetNumberOfFunctions())
{
unsigned int labelSetCount = 0;
for (int n = pvOption->GetNumberOfFunctions() - 1; n >= 0; n--)
{
typename SegmentationFilterType::PartialVolumeLabelSetType labelSet =
parser->ConvertVector<LabelType>(pvOption->GetFunction(n)->GetName());
if (labelSet.size() != 2)
{
if (verbose)
{
std::cerr << "Error: Currently Atropos only supports partial "
<< "volume label sets of size equal to 2." << std::endl;
}
return EXIT_FAILURE;
}
segmenter->AddPartialVolumeLabelSet(labelSet);
using SampleType = typename SegmentationFilterType::SampleType;
using LikelihoodType = itk::ants::Statistics::PartialVolumeGaussianListSampleFunction<SampleType, float, float>;
typename LikelihoodType::Pointer partialVolumeLikelihood = LikelihoodType::New();
segmenter->SetLikelihoodFunction(labelSetCount + segmenter->GetNumberOfTissueClasses(), partialVolumeLikelihood);
labelSetCount++;
}
typename itk::ants::CommandLineParser::OptionType::Pointer pvlOption =
parser->GetOption("use-partial-volume-likelihoods");
bool useLikelihoods = false;
if (pvlOption && pvlOption->GetNumberOfFunctions())
{
std::string value = pvlOption->GetFunction(0)->GetName();
ConvertToLowerCase(value);
if (!value.compare("true") || !value.compare("1"))
{
useLikelihoods = true;
}
else
{
useLikelihoods = false;
}
}
segmenter->SetUsePartialVolumeLikelihoods(useLikelihoods);
}
/**
* outliers handling
*/
typename itk::ants::CommandLineParser::OptionType::Pointer outlierOption = parser->GetOption("winsorize-outliers");
if (outlierOption && outlierOption->GetNumberOfFunctions())
{
std::string outlierStrategy = outlierOption->GetFunction(0)->GetName();
ConvertToLowerCase(outlierStrategy);
if (!outlierStrategy.compare(std::string("boxplot")))
{
using SampleType = typename SegmentationFilterType::SampleType;
using SampleFilterType = itk::ants::Statistics::BoxPlotQuantileListSampleFilter<SampleType>;
typename SampleFilterType::Pointer boxplotFilter = SampleFilterType::New();
if (outlierOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
boxplotFilter->SetLowerPercentile(parser->Convert<float>(outlierOption->GetFunction(0)->GetParameter(0)));
}
if (outlierOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
boxplotFilter->SetUpperPercentile(parser->Convert<float>(outlierOption->GetFunction(0)->GetParameter(1)));
}
if (outlierOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
boxplotFilter->SetWhiskerScalingFactor(parser->Convert<float>(outlierOption->GetFunction(0)->GetParameter(2)));
}
segmenter->SetOutlierHandlingFilter(boxplotFilter);
}
else if (!outlierStrategy.compare(std::string("grubbsrosner")))
{
using SampleType = typename SegmentationFilterType::SampleType;
using SampleFilterType = itk::ants::Statistics::GrubbsRosnerListSampleFilter<SampleType>;
typename SampleFilterType::Pointer grubbsFilter = SampleFilterType::New();
if (outlierOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
grubbsFilter->SetSignificanceLevel(parser->Convert<float>(outlierOption->GetFunction(0)->GetParameter(0)));
}
if (outlierOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
grubbsFilter->SetWinsorizingLevel(parser->Convert<float>(outlierOption->GetFunction(0)->GetParameter(1)));
}
segmenter->SetOutlierHandlingFilter(grubbsFilter);
}
else
{