The STAPLE filter implements the Simultaneous Truth and Performance Level Estimation algorithm for generating ground truth volumes from a set of binary expert segmentations.
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Image | Execute (const std::vector< Image > &images) |
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Image | Execute (const Image &image1) |
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Image | Execute (const Image &image1, const Image &image2) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4, const Image &image5) |
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Image | Execute (const std::vector< Image > &images, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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Image | Execute (const Image &image1, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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Image | Execute (const Image &image1, const Image &image2, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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Image | Execute (const Image &image1, const Image &image2, const Image &image3, const Image &image4, const Image &image5, double confidenceWeight, double foregroundValue, unsigned int maximumIterations) |
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double | GetConfidenceWeight () const |
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uint32_t | GetElapsedIterations () const |
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double | GetForegroundValue () const |
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unsigned int | GetMaximumIterations () const |
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std::string | GetName () const |
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std::vector< double > | GetSensitivity () const |
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std::vector< double > | GetSpecificity () const |
| After the filter is updated, this method returns the Specificity (true negative fraction, q) value for the i-th expert input volume. More...
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Self & | SetConfidenceWeight (double ConfidenceWeight) |
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Self & | SetForegroundValue (double ForegroundValue) |
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Self & | SetMaximumIterations (unsigned int MaximumIterations) |
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| STAPLEImageFilter () |
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std::string | ToString () const |
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virtual | ~STAPLEImageFilter () |
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| ImageFilter () |
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virtual | ~ImageFilter ()=0 |
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virtual void | Abort () |
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virtual int | AddCommand (itk::simple::EventEnum event, itk::simple::Command &cmd) |
| Add a Command Object to observer the event. More...
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virtual float | GetProgress () const |
| An Active Measurement of the progress of execution. More...
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virtual bool | HasCommand (itk::simple::EventEnum event) const |
| Query of this object has any registered commands for event. More...
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| ProcessObject () |
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virtual void | RemoveAllCommands () |
| Remove all registered commands. More...
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virtual | ~ProcessObject () |
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virtual void | DebugOn () |
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virtual void | DebugOff () |
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virtual bool | GetDebug () const |
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virtual void | SetDebug (bool debugFlag) |
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virtual void | SetNumberOfThreads (unsigned int n) |
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virtual unsigned int | GetNumberOfThreads () const |
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static bool | GetGlobalDefaultDebug () |
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static void | GlobalDefaultDebugOff () |
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static void | GlobalDefaultDebugOn () |
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static void | SetGlobalDefaultDebug (bool debugFlag) |
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static void | GlobalWarningDisplayOn () |
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static void | GlobalWarningDisplayOff () |
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static void | SetGlobalWarningDisplay (bool flag) |
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static bool | GetGlobalWarningDisplay () |
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static void | SetGlobalDefaultNumberOfThreads (unsigned int n) |
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static unsigned int | GetGlobalDefaultNumberOfThreads () |
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static double | GetGlobalDefaultCoordinateTolerance () |
| Access the global tolerance to determine congruent spaces. More...
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static void | SetGlobalDefaultCoordinateTolerance (double) |
| Access the global tolerance to determine congruent spaces. More...
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static double | GetGlobalDefaultDirectionTolerance () |
| Access the global tolerance to determine congruent spaces. More...
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static void | SetGlobalDefaultDirectionTolerance (double) |
| Access the global tolerance to determine congruent spaces. More...
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virtual unsigned long | AddITKObserver (const itk::EventObject &, itk::Command *) |
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virtual itk::ProcessObject * | GetActiveProcess () |
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virtual void | OnActiveProcessDelete () |
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virtual void | onCommandDelete (const itk::simple::Command *cmd) SITK_NOEXCEPT |
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virtual void | PreUpdate (itk::ProcessObject *p) |
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virtual void | RemoveITKObserver (EventCommand &e) |
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| NonCopyable () |
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static void | FixNonZeroIndex (TImageType *img) |
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template<class TImageType > |
static TImageType::ConstPointer | CastImageToITK (const Image &img) |
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template<class TImageType > |
static Image | CastITKToImage (TImageType *img) |
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template<class TPixelType , unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector> |
static Image | CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img) |
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static const itk::EventObject & | GetITKEventObject (EventEnum e) |
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template<typename T > |
static std::ostream & | ToStringHelper (std::ostream &os, const T &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const char &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const signed char &v) |
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static std::ostream & | ToStringHelper (std::ostream &os, const unsigned char &v) |
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The STAPLE filter implements the Simultaneous Truth and Performance Level Estimation algorithm for generating ground truth volumes from a set of binary expert segmentations.
The STAPLE algorithm treats segmentation as a pixelwise classification, which leads to an averaging scheme that accounts for systematic biases in the behavior of experts in order to generate a fuzzy ground truth volume and simultaneous accuracy assessment of each expert. The ground truth volumes produced by this filter are floating point volumes of values between zero and one that indicate probability of each pixel being in the object targeted by the segmentation.
The STAPLE algorithm is described in
S. Warfield, K. Zou, W. Wells, "Validation of image segmentation and expert
quality with an expectation-maximization algorithm" in MICCAI 2002: Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, Heidelberg, Germany, 2002, pp. 298-306
- INPUTS
- Input volumes to the STAPLE filter must be binary segmentations of an image, that is, there must be a single foreground value that represents positively classified pixels (pixels that are considered to belong inside the segmentation). Any number of background pixel values may be present in the input images. You can, for example, input volumes with many different labels as long as the structure you are interested in creating ground truth for is consistently labeled among all input volumes. Pixel type of the input volumes does not matter. Specify the label value for positively classified pixels using SetForegroundValue. All other labels will be considered to be negatively classified pixels (background).
Input volumes must all contain the same size RequestedRegions.
- OUTPUTS
- The STAPLE filter produces a single output volume with a range of floating point values from zero to one. IT IS VERY IMPORTANT TO INSTANTIATE THIS FILTER WITH A FLOATING POINT OUTPUT TYPE (floats or doubles). You may threshold the output above some probability threshold if you wish to produce a binary ground truth.
- PARAMETERS
- The STAPLE algorithm requires a number of inputs. You may specify any number of input volumes using the SetInput(i, p_i) method, where i ranges from zero to N-1, N is the total number of input segmentations, and p_i is the SmartPointer to the i-th segmentation.
The SetConfidenceWeight parameter is a modifier for the prior probability that any pixel would be classified as inside the target object. This implementation of the STAPLE algorithm automatically calculates prior positive classification probability as the average fraction of the image volume filled by the target object in each input segmentation. The ConfidenceWeight parameter allows for scaling the of this default prior probability: if g_t is the prior probability that a pixel would be classified inside the target object, then g_t is set to g_t * ConfidenceWeight before iterating on the solution. In general ConfidenceWeight should be left to the default of 1.0.
You must provide a foreground value using SetForegroundValue that the STAPLE algorithm will use to identify positively classified pixels in the the input images. All other values in the image will be treated as background values. For example, if your input segmentations consist of 1's everywhere inside the segmented region, then use SetForegroundValue(1).
The STAPLE algorithm is an iterative E-M algorithm and will converge on a solution after some number of iterations that cannot be known a priori. After updating the filter, the total elapsed iterations taken to converge on the solution can be queried through GetElapsedIterations() . You may also specify a MaximumNumberOfIterations, after which the algorithm will stop iterating regardless of whether or not it has converged. This implementation of the STAPLE algorithm will find the solution to within seven digits of precision unless it is stopped early.
Once updated, the Sensitivity (true positive fraction, q) and Specificity (true negative fraction, q) for each expert input volume can be queried using GetSensitivity(i) and GetSpecificity(i), where i is the i-th input volume.
- REQUIRED PARAMETERS
- The only required parameters for this filter are the ForegroundValue and the input volumes. All other parameters may be safely left to their default values. Please see the paper cited above for more information on the STAPLE algorithm and its parameters. A proper understanding of the algorithm is important for interpreting the results that it produces.
- EVENTS
- This filter invokes IterationEvent() at each iteration of the E-M algorithm. Setting the AbortGenerateData() flag will cause the algorithm to halt after the current iteration and produce results just as if it had converged. The algorithm makes no attempt to report its progress since the number of iterations needed cannot be known in advance.
- See also
- itk::simple::STAPLE for the procedural interface
Definition at line 73 of file sitkSTAPLEImageFilter.h.