PyRadiomics wrapper to implement custom functions and utilities

pyradiomics_radler/__init__.py

+import radiomics
+import pkgutil
+import os
+import sys
+import inspect
+
+from .myfeatureextractor import MyRadiomicsFeaturesExtractor
+
+
+def getFeatureClasses():
+    """
+    Iterates over all modules of the radiomics package using pkgutil and subsequently imports those modules.
+
+    Return a dictionary of all modules containing featureClasses, with modulename as key, abstract
+    class object of the featureClass as value. Assumes only one featureClass per module
+
+    This is achieved by inspect.getmembers. Modules are added if it contains a member that is a class,
+    with name starting with 'Radiomics' and is inherited from :py:class:`radiomics.base.RadiomicsFeaturesBase`.
+
+    This iteration only runs once (at initialization of toolbox), subsequent calls return the dictionary created by the
+    first call.
+    """
+    global _featureClasses
+    if (
+        _featureClasses is None
+    ):  # On first call, enumerate possible feature classes and import PyRadiomics modules
+        _featureClasses = {}
+        for _, mod, _ in pkgutil.iter_modules([os.path.dirname(__file__)]):
+            if str(mod).startswith(
+                '_'
+            ):  # Skip loading of 'private' classes, these don't contain feature classes
+                continue
+            __import__('pyradiomics_radler.' + mod)
+            module = sys.modules['pyradiomics_radler.' + mod]
+            attributes = inspect.getmembers(module, inspect.isclass)
+            for a in attributes:
+                if a[0].startswith('MyRadiomics'):
+                    for parentClass in inspect.getmro(a[1])[
+                        1:
+                    ]:  # only include classes that inherit from RadiomicsFeaturesBase
+                        if parentClass.__name__ == 'MyRadiomicsFeaturesBase':
+                            _featureClasses[mod] = a[1]
+                            break
+
+    return _featureClasses
+
+
+_featureClasses = None
+_featureClasses = getFeatureClasses()
+
+newFeatureClasses = radiomics._featureClasses
+for f, cl in _featureClasses.items():
+    newFeatureClasses[f] = cl
+radiomics._featureClasses = newFeatureClasses

pyradiomics_radler/mybase.py

+from radiomics.base import RadiomicsFeaturesBase
+
+from . import myimageoperations as imageoperations
+import numpy
+
+
+class MyRadiomicsFeaturesBase(RadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        self.binMode = kwargs.get('binMode', 'uniform')
+        super().__init__(inputImage, inputMask, **kwargs)
+
+    def _applyBinning(self):
+        self.matrix, self.binEdges = imageoperations.binImage(
+            self.binWidth,
+            self.imageArray,
+            self.maskArray,
+            self.settings.get('binCount', None),
+            self.binMode,
+        )
+
+        self.coefficients['grayLevels'] = numpy.unique(self.matrix[self.maskArray])
+        self.coefficients['Ng'] = int(
+            numpy.max(self.coefficients['grayLevels'])
+        )  # max gray level in the ROI

pyradiomics_radler/myfeatureextractor.py

+from radiomics.featureextractor import RadiomicsFeaturesExtractor
+from . import myimageoperations as imageoperations
+
+import collections
+import six
+from itertools import chain
+
+
+class MyRadiomicsFeaturesExtractor(RadiomicsFeaturesExtractor):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def execute(self, imageFilepath, maskFilepath, label=None, voxelBased=False):
+        """
+        Compute radiomics signature for provide image and mask combination. It comprises of the following steps:
+
+        1. Image and mask are loaded and normalized/resampled if necessary.
+        2. Validity of ROI is checked using :py:func:`~imageoperations.checkMask`, which also computes and returns the
+            bounding box.
+        3. If enabled, provenance information is calculated and stored as part of the result. (Not available in voxel-based
+            extraction)
+        4. Shape features are calculated on a cropped (no padding) version of the original image. (Not available in
+            voxel-based extraction)
+        5. If enabled, resegment the mask based upon the range specified in ``resegmentRange`` (default None: resegmentation
+            disabled).
+        6. Other enabled feature classes are calculated using all specified image types in ``_enabledImageTypes``. Images
+            are cropped to tumor mask (no padding) after application of any filter and before being passed to the feature
+            class.
+        7. The calculated features is returned as ``collections.OrderedDict``.
+
+        :param imageFilepath: SimpleITK Image, or string pointing to image file location
+        :param maskFilepath: SimpleITK Image, or string pointing to labelmap file location
+        :param label: Integer, value of the label for which to extract features. If not specified, last specified label
+            is used. Default label is 1.
+        :returns: dictionary containing calculated signature ("<imageType>_<featureClass>_<featureName>":value).
+        """
+
+        if self.geometryTolerance != self.settings.get('geometryTolerance'):
+            self._setTolerance()
+
+        if label is not None:
+            self.settings['label'] = label
+
+        self.settings['voxelBased'] = voxelBased
+        if voxelBased:
+            self.logger.info('Starting voxel based extraction')
+
+        self.logger.info('Calculating features with label: %d', self.settings['label'])
+        self.logger.debug('Enabled images types: %s', self._enabledImagetypes)
+        self.logger.debug('Enabled features: %s', self._enabledFeatures)
+        self.logger.debug('Current settings: %s', self.settings)
+
+        if self.settings.get('binCount', None) is not None:
+            self.logger.warning(
+                'Fixed bin Count enabled! However, we recommend using a fixed bin Width. See '
+                'http://pyradiomics.readthedocs.io/en/latest/faq.html#radiomics-fixed-bin-width for more '
+                'details'
+            )
+
+        # 1. Load the image and mask
+        featureVector = collections.OrderedDict()
+        image, mask = self.loadImage(imageFilepath, maskFilepath)
+
+        if image is None or mask is None:
+            # No features can be extracted, return the empty featureVector
+            return featureVector
+
+        # 2. Check whether loaded mask contains a valid ROI for feature extraction and get bounding box
+        boundingBox, correctedMask = imageoperations.checkMask(
+            image, mask, **self.settings
+        )
+
+        # Update the mask if it had to be resampled
+        if correctedMask is not None:
+            mask = correctedMask
+
+        if boundingBox is None:
+            # Mask checks failed, do not extract features and return the empty featureVector
+            return featureVector
+
+        self.logger.debug('Image and Mask loaded and valid, starting extraction')
+
+        if not voxelBased:
+            # 3. Add the additional information if enabled
+            if self.settings['additionalInfo']:
+                featureVector.update(
+                    self.getProvenance(imageFilepath, maskFilepath, mask)
+                )
+
+            # 4. If shape descriptors should be calculated, handle it separately here
+            if 'shape' in self._enabledFeatures.keys():
+                croppedImage, croppedMask = imageoperations.cropToTumorMask(
+                    image, mask, boundingBox
+                )
+                enabledFeatures = self._enabledFeatures['shape']
+                self.logger.info('Computing shape')
+                shapeClass = self.featureClasses['shape'](
+                    croppedImage, croppedMask, **self.settings
+                )
+                if enabledFeatures is None or len(enabledFeatures) == 0:
+                    shapeClass.enableAllFeatures()
+                else:
+                    for feature in enabledFeatures:
+                        shapeClass.enableFeatureByName(feature)
+
+                for (featureName, featureValue) in six.iteritems(shapeClass.execute()):
+                    newFeatureName = 'original_shape_%s' % featureName
+                    featureVector[newFeatureName] = featureValue
+
+        # 5. Resegment the mask if enabled (parameter regsegmentMask is not None)
+        resegmentRange = self.settings.get('resegmentRange', None)
+        if resegmentRange is not None:
+            resegmentedMask = imageoperations.resegmentMask(
+                image, mask, resegmentRange, self.settings['label']
+            )
+
+            # Recheck to see if the mask is still valid
+            boundingBox, correctedMask = imageoperations.checkMask(
+                image, resegmentedMask, **self.settings
+            )
+            # Update the mask if it had to be resampled
+            if correctedMask is not None:
+                resegmentedMask = correctedMask
+
+            if boundingBox is None:
+                # Mask checks failed, do not extract features and return the empty featureVector
+                return featureVector
+
+            # Resegmentation successful
+            mask = resegmentedMask
+
+        # 6. Calculate other enabled feature classes using enabled image types
+        # Make generators for all enabled image types
+        self.logger.debug('Creating image type iterator')
+        imageGenerators = []
+        for imageType, customKwargs in six.iteritems(self._enabledImagetypes):
+            args = self.settings.copy()
+            args.update(customKwargs)
+            self.logger.info(
+                'Adding image type "%s" with custom settings: %s'
+                % (imageType, str(customKwargs))
+            )
+            imageGenerators = chain(
+                imageGenerators,
+                getattr(imageoperations, 'get%sImage' % imageType)(image, mask, **args),
+            )
+
+        self.logger.debug('Extracting features')
+        # Calculate features for all (filtered) images in the generator
+        for inputImage, imageTypeName, inputKwargs in imageGenerators:
+            self.logger.info('Calculating features for %s image', imageTypeName)
+            inputImage, inputMask = imageoperations.cropToTumorMask(
+                inputImage, mask, boundingBox
+            )
+            featureVector.update(
+                self.computeFeatures(
+                    inputImage, inputMask, imageTypeName, **inputKwargs
+                )
+            )
+
+        self.logger.debug('Features extracted')
+
+        return featureVector

pyradiomics_radler/myglcm.py

+from .mybase import MyRadiomicsFeaturesBase
+from radiomics.glcm import RadiomicsGLCM
+
+
+class MyRadiomicsGLCM(RadiomicsGLCM, MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+

pyradiomics_radler/mygldm.py

+from .mybase import MyRadiomicsFeaturesBase
+from radiomics.gldm import RadiomicsGLDM
+
+
+class MyRadiomicsGLDM(RadiomicsGLDM, MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+

pyradiomics_radler/myglrlm.py

+from .mybase import MyRadiomicsFeaturesBase
+from radiomics.glrlm import RadiomicsGLRLM
+
+import numpy
+
+
+class MyRadiomicsGLRLM(RadiomicsGLRLM, MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+
+    def getGrayLevelNonUniformityFeatureValue(self):
+        r"""
+        **3. Gray Level Non-Uniformity (GLN)**
+
+        .. math::
+        \textit{GLN} = \frac{\sum^{N_g}_{i=1}\left(\sum^{N_r}_{j=1}{\textbf{P}(i,j|\theta)}\right)^2}{N_z(\theta)}
+
+        GLN measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a
+        greater similarity in intensity values.
+        """
+
+        pg = self.coefficients['pg']
+        Nz = self.coefficients['Nz']
+
+        gln = numpy.sum(
+            (pg ** 2), 0
+        )  # NB the division by Nz has been removed according to valieres et al
+        return gln.mean()
+
+    def getRunLengthNonUniformityFeatureValue(self):
+        r"""
+        **5. Run Length Non-Uniformity (RLN)**
+
+        .. math::
+        \textit{RLN} = \frac{\sum^{N_r}_{j=1}\left(\sum^{N_g}_{i=1}{\textbf{P}(i,j|\theta)}\right)^2}{N_z(\theta)}
+
+        RLN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity
+        among run lengths in the image.
+        """
+        pr = self.coefficients['pr']
+        Nz = self.coefficients['Nz']
+
+        # NB the division by Nz has been removed according to Vallieres et al
+        rln = numpy.sum((pr ** 2), 0)  # / Nz
+        return rln.mean()
+

pyradiomics_radler/myglszm.py

+from .mybase import MyRadiomicsFeaturesBase
+from radiomics.glszm import RadiomicsGLSZM
+
+
+class MyRadiomicsGLSZM(RadiomicsGLSZM, MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+

pyradiomics_radler/myimageoperations.py

+import logging
+
+from radiomics.imageoperations import *
+
+logger = logging.getLogger(__name__)
+
+
+def binImage(
+    binwidth,
+    parameterMatrix,
+    parameterMatrixCoordinates=None,
+    bincount=None,
+    mode='uniform',
+):
+    r"""
+    Discretizes the parameterMatrix (matrix representation of the gray levels in the ROI) using the binEdges calculated
+    using :py:func:`getBinEdges`. Only voxels defined by parameterMatrixCoordinates (defining the segmentation) are used
+    for calculation of histogram and subsequently discretized. Voxels outside segmentation are left unchanged.
+
+    :math:`X_{b, i} = \lfloor \frac{X_{gl, i}}{W} \rfloor - \lfloor \frac {\min(X_{gl})}{W} \rfloor + 1`
+
+    Here, :math:`X_{gl, i}` and :math:`X_{b, i}` are gray level intensities before and after discretization, respectively.
+    :math:`{W}` is the bin width value (specfied in ``binWidth`` parameter). The first part of the formula ensures that
+    the bins are equally spaced from 0, whereas the second part ensures that the minimum gray level intensity inside the
+    ROI after binning is always 1.
+
+    If the range of gray level intensities is equally dividable by the binWidth, i.e. :math:`(\max(X_{gl})- \min(X_{gl}))
+    \mod W = 0`, the maximum intensity will be encoded as numBins + 1, therefore the maximum number of gray
+    level intensities in the ROI after binning is number of bins + 1.
+
+    .. warning::
+        This is different from the assignment of voxels to the bins by ``numpy.histogram`` , which has half-open bins, with
+        the exception of the rightmost bin, which means this maximum values are assigned to the topmost bin.
+        ``numpy.digitize`` uses half-open bins, including the rightmost bin.
+
+    .. note::
+        This method is slightly different from the fixed bin size discretization method described by IBSI. The two most
+        notable differences are 1) that PyRadiomics uses a floor division (and adds 1), as opposed to a ceiling division and
+        2) that in PyRadiomics, bins are always equally spaced from 0, as opposed to equally spaced from the minimum
+        gray level intensity.
+    """
+    global logger
+    logger.debug('Discretizing gray levels inside ROI')
+
+    if parameterMatrixCoordinates is None:
+        if bincount is not None:
+            if mode == 'uniform':
+                binEdges = numpy.histogram(parameterMatrix[:], bincount)[1]
+            elif mode == 'equal':
+                binEdges = numpy.percentile(
+                    parameterMatrix[:], numpy.arange(0, 1, 1 / bincount)
+                )
+                binEdges = numpy.r_[binEdges, numpy.max(parameterMatrix[:])]
+                # check monotonicity
+                diff = numpy.diff(binEdges)
+                while not (diff >= 0).all():
+                    idx_non_monotonical = numpy.where(diff < 0)[0]
+                    binEdges = numpy.delete(binEdges, idx_non_monotonical)
+                    diff = numpy.diff(binEdges)
+        else:
+            binEdges = getBinEdges(binwidth, parameterMatrix[:])
+        parameterMatrix = numpy.digitize(parameterMatrix, binEdges)
+    else:
+        if bincount is not None:
+            if mode == 'uniform':
+                binEdges = numpy.histogram(
+                    parameterMatrix[parameterMatrixCoordinates], bincount
+                )[1]
+            elif mode == 'equal':
+                binEdges = numpy.percentile(
+                    parameterMatrix[parameterMatrixCoordinates],
+                    numpy.arange(0, 1, 1 / bincount),
+                )
+                binEdges = numpy.r_[binEdges, numpy.max(parameterMatrix[:])]
+                # check monotonicity
+                diff = numpy.diff(binEdges)
+                while not (diff >= 0).all():
+                    idx_non_monotonical = numpy.where(diff < 0)[0]
+                    binEdges = numpy.delete(binEdges, idx_non_monotonical)
+                    diff = numpy.diff(binEdges)
+
+        else:
+            binEdges = getBinEdges(
+                binwidth, parameterMatrix[parameterMatrixCoordinates]
+            )
+        parameterMatrix[parameterMatrixCoordinates] = numpy.digitize(
+            parameterMatrix[parameterMatrixCoordinates], binEdges
+        )
+
+    return parameterMatrix, binEdges
+
+
+def checkMask(imageNode, maskNode, **kwargs):
+    """
+    Checks whether the Region of Interest (ROI) defined in the mask size and dimensions match constraints, specified in
+    settings. The following checks are performed.
+
+    1. Check whether the mask corresponds to the image (i.e. has a similar size, spacing, direction and origin). **N.B.
+        This check is performed by SimpleITK, if it fails, an error is logged, with additional error information from
+        SimpleITK logged with level DEBUG (i.e. logging-level has to be set to debug to store this information in the log
+        file).** The tolerance can be increased using the ``geometryTolerance`` parameter. Alternatively, if the
+        ``correctMask`` parameter is ``True``, PyRadiomics will check if the mask contains a valid ROI (inside image
+        physical area) and if so, resample the mask to image geometry. See :ref:`radiomics-settings-label` for more info.
+
+    2. Check if the label is present in the mask
+    3. Count the number of dimensions in which the size of the ROI > 1 (i.e. does the ROI represent a single voxel (0), a
+        line (1), a surface (2) or a volume (3)) and compare this to the minimum number of dimension required (specified in
+        ``minimumROIDimensions``).
+    4. Optional. Check if there are at least N voxels in the ROI. N is defined in ``minimumROISize``, this test is skipped
+        if ``minimumROISize = None``.
+
+    This function returns a tuple of two items. The first item (if not None) is the bounding box of the mask. The second
+    item is the mask that has been corrected by resampling to the input image geometry (if that resampling was successful).
+
+    If a check fails, an error is logged and a (None,None) tuple is returned. No features will be extracted for this mask.
+    If the mask passes all tests, this function returns the bounding box, which is used in the :py:func:`cropToTumorMask`
+    function.
+
+    The bounding box is calculated during (1.) and used for the subsequent checks. The bounding box is
+    calculated by SimpleITK.LabelStatisticsImageFilter() and returned as a tuple of indices: (L_x, U_x, L_y, U_y, L_z,
+    U_z), where 'L' and 'U' are lower and upper bound, respectively, and 'x', 'y' and 'z' the three image dimensions.
+
+    By reusing the bounding box calculated here, calls to SimpleITK.LabelStatisticsImageFilter() are reduced, improving
+    performance.
+
+    Uses the following settings:
+
+    - minimumROIDimensions [1]: Integer, range 1-3, specifies the minimum dimensions (1D, 2D or 3D, respectively).
+        Single-voxel segmentations are always excluded.
+    - minimumROISize [None]: Integer, > 0,  specifies the minimum number of voxels required. Test is skipped if
+        this parameter is set to None.
+
+    .. note::
+
+        If the first check fails there are generally 2 possible causes:
+
+        1. The image and mask are matched, but there is a slight difference in origin, direction or spacing. The exact
+            cause, difference and used tolerance are stored with level DEBUG in a log (if enabled). For more information on
+            setting up logging, see ":ref:`setting up logging <radiomics-logging-label>`" and the helloRadiomics examples
+            (located in the ``pyradiomics/examples`` folder). This problem can be fixed by changing the global tolerance
+            (``geometryTolerance`` parameter) or enabling mask correction (``correctMask`` parameter).
+        2. The image and mask do not match, but the ROI contained within the mask does represent a physical volume
+            contained within the image. If this is the case, resampling is needed to ensure matching geometry between image
+            and mask before features can be extracted. This can be achieved by enabling mask correction using the
+            ``correctMask`` parameter.
+    """
+
+    global logger
+    boundingBox = None
+    correctedMask = None
+
+    label = kwargs.get('label', 1)
+    minDims = kwargs.get('minimumROIDimensions', 1)
+    minSize = kwargs.get('minimumROISize', None)
+
+    logger.debug('Checking mask with label %d', label)
+    logger.debug('Calculating bounding box')
+    # Determine bounds
+    lsif = sitk.LabelStatisticsImageFilter()
+    try:
+        lsif.Execute(imageNode, maskNode)
+
+        # If lsif fails, and mask is corrected, it includes a check whether the label is present. Therefore, perform
+        # this test here only if lsif does not fail on the first attempt.
+        if label not in lsif.GetLabels():
+            logger.error('Label (%g) not present in mask', label)
+            return (boundingBox, correctedMask)
+    except RuntimeError as e:
+        # If correctMask = True, try to resample the mask to the image geometry, otherwise return None ("fail")
+        if not kwargs.get('correctMask', False):
+            if (
+                "Both images for LabelStatisticsImageFilter don't match type or dimension!"
+                in e.args[0]
+            ):
+                logger.error(
+                    'Image/Mask datatype or size mismatch. Potential solution: enable correctMask, see '
+                    'Documentation:Usage:Customizing the Extraction:Settings:correctMask for more information'
+                )
+                logger.debug('Additional information on error.', exc_info=True)
+            elif "Inputs do not occupy the same physical space!" in e.args[0]:
+                logger.error(
+                    'Image/Mask geometry mismatch. Potential solution: increase tolerance using geometryTolerance, '
+                    'see Documentation:Usage:Customizing the Extraction:Settings:geometryTolerance for more '
+                    'information'
+                )
+                logger.debug('Additional information on error.', exc_info=True)
+            return (boundingBox, correctedMask)
+
+        logger.warning('Image/Mask geometry mismatch, attempting to correct Mask')
+
+        correctedMask = _correctMask(imageNode, maskNode, label)
+        if correctedMask is None:  # Resampling failed (ROI outside image physical space
+            logger.error(
+                'Image/Mask correction failed, ROI invalid (not found or outside of physical image bounds)'
+            )
+            return boundingBox, correctedMask
+
+        # Resampling succesful, try to calculate boundingbox
+        try:
+            lsif.Execute(imageNode, correctedMask)
+        except RuntimeError:
+            logger.error(
+                'Calculation of bounding box failed, for more information run with DEBUG logging and check log'
+            )
+            logger.debug(
+                'Bounding box calculation with resampled mask failed', exc_info=True
+            )
+            return boundingBox, correctedMask
+
+    # LBound and UBound of the bounding box, as (L_X, U_X, L_Y, U_Y, L_Z, U_Z)
+    boundingBox = numpy.array(lsif.GetBoundingBox(label))
+
+    logger.debug('Checking minimum number of dimensions requirements (%d)', minDims)
+    ndims = numpy.sum(
+        (boundingBox[1::2] - boundingBox[0::2] + 1) > 1
+    )  # UBound - LBound + 1 = Size
+    if ndims < minDims:
+        logger.error(
+            'mask has too few dimensions (number of dimensions %d, minimum required %d)',
+            ndims,
+            minDims,
+        )
+        return None, correctedMask
+
+    if minSize is not None:
+        logger.debug('Checking minimum size requirements (minimum size: %d)', minSize)
+        roiSize = lsif.GetCount(label)
+        if roiSize <= minSize:
+            logger.error(
+                'Size of the ROI is too small (minimum size: %g, ROI size: %g',
+                minSize,
+                roiSize,
+            )
+            return None, correctedMask
+
+    return boundingBox, correctedMask
+

pyradiomics_radler/myngtdm.py

+from .mybase import MyRadiomicsFeaturesBase
+from radiomics.ngtdm import RadiomicsNGTDM
+
+
+class MyRadiomicsNGTDM(RadiomicsNGTDM, MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+

pyradiomics_radler/myshape.py

+import numpy
+from scipy.spatial import ConvexHull
+from radiomics.shape import RadiomicsShape
+from .mybase import MyRadiomicsFeaturesBase
+
+
+class MyRadiomicsShape(MyRadiomicsFeaturesBase):
+    def __init__(self, inputImage, inputMask, **kwargs):
+        super().__init__(inputImage, inputMask, **kwargs)
+
+    def _initVoxelBasedCalculation(self):
+        RadiomicsShape._initVoxelBasedCalculation(self)
+
+    def _initSegmentBasedCalculation(self):
+        RadiomicsShape._initSegmentBasedCalculation(self)
+
+    def _calculateSurfaceArea(self):
+        self.SurfaceArea = RadiomicsShape._calculateSurfaceArea(self)
+
+    def calculateDiameters(self):
+        RadiomicsShape.calculateDiameters(self)
+
+    def _interpolate(self, grid, p1, p2):
+        RadiomicsShape._interpolate(self, grid, p1, p2)
+
+    def getCompactness2FeatureValue(self):
+        r"""
+        **6. Compactness 2**
+
+        .. math::
+        \textit{compactness 2} = 36 \pi \frac{V^2}{A^3}
+
+        Similar to Sphericity and Compactness 1, Compactness 2 is a measure of how compact the shape of the tumor is