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Extend resegmenting functionality #420

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merged 2 commits into from
Sep 10, 2018
Merged

Extend resegmenting functionality #420

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b3a1504
ENH: Extend resegmenting functionality
JoostJM Aug 28, 2018
c44fa15
ENH: Update provenance info, use error raising
JoostJM Aug 28, 2018
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91 changes: 42 additions & 49 deletions radiomics/featureextractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,8 @@ def __init__(self, *args, **kwargs):

self.featureClasses = getFeatureClasses()

self.generalInfo = None

self.settings = {}
self._enabledImagetypes = {}
self._enabledFeatures = {}
Expand Down Expand Up @@ -359,6 +361,13 @@ def execute(self, imageFilepath, maskFilepath, label=None, voxelBased=False):
if label is not None:
self.settings['label'] = label

if self.settings['additionalInfo']:
self.generalInfo = generalinfo.GeneralInfo()
self.generalInfo.addGeneralSettings(self.settings)
self.generalInfo.addEnabledImageTypes(self._enabledImagetypes)
else:
self.generalInfo = None

self.settings['voxelBased'] = voxelBased
if voxelBased:
self.logger.info('Starting voxel based extraction')
Expand All @@ -377,27 +386,36 @@ def execute(self, imageFilepath, maskFilepath, label=None, voxelBased=False):
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
# Raises a ValueError if the ROI is invalid
boundingBox, correctedMask = imageoperations.checkMask(image, mask, **self.settings)

# Update the mask if it had to be resampled
if correctedMask is not None:
if self.generalInfo is not None:
self.generalInfo.addMaskElements(image, correctedMask, self.settings['label'], 'corrected')
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')

# 5. Resegment the mask if enabled (parameter regsegmentMask is not None)
resegmentedMask = None
resegmentRange = self.settings.get('resegmentRange', None)
if resegmentRange is not None:
resegmentMode = self.settings.get('resegmentMode', 'absolute')
resegmentedMask = imageoperations.resegmentMask(image, mask, resegmentRange, resegmentMode,
self.settings['label'])

# Recheck to see if the mask is still valid, raises a ValueError if not
boundingBox, correctedMask = imageoperations.checkMask(image, resegmentedMask, **self.settings)

if self.generalInfo is not None:
self.generalInfo.addMaskElements(image, resegmentedMask, self.settings['label'], 'resegmented')

if not voxelBased:
# 3. Add the additional information if enabled
if self.settings['additionalInfo']:
featureVector.update(self.getProvenance(imageFilepath, maskFilepath, mask))
if self.generalInfo is not None:
featureVector.update(self.generalInfo.getGeneralInfo())

# 4. If shape descriptors should be calculated, handle it separately here
if 'shape' in self._enabledFeatures.keys():
Expand All @@ -416,22 +434,8 @@ def execute(self, imageFilepath, maskFilepath, label=None, voxelBased=False):
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
# Only use resegemented mask for feature classes other than shape
if resegmentedMask is not None:
mask = resegmentedMask

# 6. Calculate other enabled feature classes using enabled image types
Expand Down Expand Up @@ -474,16 +478,18 @@ def loadImage(self, ImageFilePath, MaskFilePath):
elif isinstance(ImageFilePath, sitk.SimpleITK.Image):
image = ImageFilePath
else:
self.logger.warning('Error reading image Filepath or SimpleITK object')
return None, None # this function is expected to always return a tuple of 2 elements
raise ValueError('Error reading image Filepath or SimpleITK object')

if isinstance(MaskFilePath, six.string_types) and os.path.isfile(MaskFilePath):
mask = sitk.ReadImage(MaskFilePath)
elif isinstance(MaskFilePath, sitk.SimpleITK.Image):
mask = MaskFilePath
else:
self.logger.warning('Error reading mask Filepath or SimpleITK object')
return None, None # this function is expected to always return a tuple of 2 elements
raise ValueError('Error reading mask Filepath or SimpleITK object')

if self.generalInfo is not None:
self.generalInfo.addImageElements(image)
self.generalInfo.addMaskElements(image, mask, self.settings.get('label', 1))

# This point is only reached if image and mask loaded correctly
if self.settings['normalize']:
Expand All @@ -495,36 +501,23 @@ def loadImage(self, ImageFilePath, MaskFilePath):
self.settings['interpolator'],
self.settings['label'],
self.settings['padDistance'])
if self.generalInfo is not None:
self.generalInfo.addImageElements(image, 'interpolated')
self.generalInfo.addMaskElements(image, mask, self.settings.get('label', 1), 'interpolated')

elif self.settings['preCrop']:
bb, correctedMask = imageoperations.checkMask(image, mask, **self.settings)
if correctedMask is not None:
# Update the mask if it had to be resampled
mask = correctedMask
if bb is None:
# Mask checks failed
return None, None
raise ValueError('Mask checks failed during pre-crop')

image, mask = imageoperations.cropToTumorMask(image, mask, bb, self.settings['padDistance'])

return image, mask

def getProvenance(self, imageFilepath, maskFilepath, mask):
"""
Generates provenance information for reproducibility. Takes the original image & mask filepath, as well as the
resampled mask which is passed to the feature classes. Returns a dictionary with keynames coded as
"general_info_<item>". For more information on generated items, see :ref:`generalinfo<radiomics-generalinfo-label>`
"""
self.logger.info('Adding additional extraction information')

provenanceVector = collections.OrderedDict()
generalinfoClass = generalinfo.GeneralInfo(imageFilepath,
maskFilepath,
mask,
self.settings,
self._enabledImagetypes)
for k, v in six.iteritems(generalinfoClass.execute()):
provenanceVector['general_info_%s' % k] = v
return provenanceVector

def computeFeatures(self, image, mask, imageTypeName, **kwargs):
"""
Compute signature using image, mask, \*\*kwargs settings.
Expand Down
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