def classify_PT(df, dcm_metadata, acquisition):
'''
Classifies a PT dicom series
Args:
df (DataFrame): A pandas DataFrame where each row is a dicom image header information
Returns:
dict: The dictionary for the PT classification
'''
log.info("Determining PT Classification...")
header_dicom = dcm_metadata['info']['header']['dicom']
series_description = header_dicom.get('SeriesDescription') or ''
classification = {}
info_object = {}
# Compute scan coverage
scan_coverage, info_object = \
common_utils.compute_scan_coverage_if_original(header_dicom, df,
info_object)
# Classify Anatomy
classification = common_utils.classify_anatomy(classification, acquisition,
series_description,
scan_coverage)
# Classify Isotope, Processing, Tracer
pt_classifier = PTClassifier(header_dicom=header_dicom,
acquisition=acquisition)
classification, info_object = pt_classifier.classify(
classification, info_object)
dcm_metadata['info'].update(info_object)
dcm_metadata['classification'] = classification
return dcm_metadata
def classify_CT(df, dcm_metadata, acquisition):
'''
Classifies a CT dicom series
Args:
df (DataFrame): A pandas DataFrame where each row is a dicom image header information
Returns:
dict: The dictionary for the CT classification
'''
log.info("Determining CT Classification...")
header_dicom = dcm_metadata['info']['header']['dicom']
series_description = header_dicom.get('SeriesDescription') or ''
classification_source = {}
classification = {}
info_object = {}
if common_utils.is_localizer(acquisition.label) or common_utils.is_localizer(series_description) or len(df) < 10:
classification['Scan Type'] = ['Localizer']
else:
classification['Scan Type'] = \
common_utils.get_scan_type_classification(
acquisition.label, header_dicom)
if not classification['Scan Type']:
classification['Scan Type'] = \
common_utils.get_scan_type_classification(
series_description, header_dicom)
# # Compute scan coverage
scan_coverage, info_object = \
common_utils.compute_scan_coverage_if_original(header_dicom, df,
info_object)
# # Reconstruction window
reconstruction_window = None
reconstruction_window = common_utils.get_reconstruction_window(
acquisition.label)
if reconstruction_window:
info_object['ReconstructionWindow'] = reconstruction_window
# # Classify Scan orientation
classification['Scan Orientation'] = common_utils.get_scan_orientation(acquisition.label)
if not classification['Scan Orientation']:
classification['Scan Orientation'] = common_utils.get_scan_orientation(
series_description)
# # Classify Anatomy
classification = common_utils.classify_anatomy(
classification, acquisition, series_description, scan_coverage)
# # Classify Contrast
classification['Contrast'] = common_utils.get_contrast_classification(
acquisition.label)
if not classification['Contrast']:
classification['Contrast'] = \
common_utils.get_contrast_classification(
series_description)
# # Set Custom classification to 'Unknown' if any of the CT classification keys is absent or empty:
if 'Scan Orientation' not in classification.keys():
classification['Custom'] = ['Unknown']
elif not classification['Scan Orientation']:
classification['Custom'] = ['Unknown']
else:
# If classification exists then create a tag in classification_source dict
# to indicate the source of Scan Orientation classification
# This classification_source dict is then added to info object
classification_source['Scan Orientation'] = 'Original'
if 'Contrast' not in classification.keys():
classification['Custom'] = ['Unknown']
elif not classification['Contrast']:
classification['Custom'] = ['Unknown']
else:
# If classification exists then create a tag in classification_source dict
# to indicate the source of Contrast classification
# This classification_source dict is then added to info object
classification_source['Contrast'] = 'Original'
if 'Anatomy' not in classification.keys():
classification['Custom'] = ['Unknown']
elif not classification['Anatomy']:
classification['Custom'] = ['Unknown']
else:
# If classification exists then create a tag in classification_source dict
# to indicate the source of Anatomy classification
# This classification_source dict is then added to info object
classification_source['Anatomy'] = 'Original'
# # Scan Coverage
if scan_coverage:
spacing_between_slices = scan_coverage / len(df)
info_object['SpacingBetweenSlices'] = round(spacing_between_slices, 2)
info_object['ClassificationSource'] = classification_source
dcm_metadata['info'].update(info_object)
dcm_metadata['classification'] = classification
return dcm_metadata
def classify_CT(df, dcm_metadata, acquisition):
'''
Classifies a CT dicom series
Args:
df (DataFrame): A pandas DataFrame where each row is a dicom image header information
Returns:
dict: The dictionary for the CT classification
'''
log.info("Determining CT Classification...")
header_dicom = dcm_metadata['info']['header']['dicom']
series_description = header_dicom.get('SeriesDescription') or ''
classification = {}
info_object = {}
if common_utils.is_localizer(
acquisition.label) or common_utils.is_localizer(
series_description) or len(df) < 10:
classification['Scan Type'] = ['Localizer']
else:
classification['Scan Type'] = \
common_utils.get_scan_type_classification(
acquisition.label, header_dicom)
if not classification['Scan Type']:
classification['Scan Type'] = \
common_utils.get_scan_type_classification(
series_description, header_dicom)
# Compute scan coverage
scan_coverage, info_object = \
common_utils.compute_scan_coverage_if_original(header_dicom, df,
info_object)
# # Reconstruction window
reconstruction_window = None
reconstruction_window = common_utils.get_reconstruction_window(
acquisition.label)
if reconstruction_window:
info_object['ReconstructionWindow'] = reconstruction_window
# # Scan orientation
scan_orientation = None
scan_orientation = common_utils.get_scan_orientation(acquisition.label)
if scan_orientation:
info_object['ScanOrientation'] = scan_orientation
else:
scan_orientation = common_utils.get_scan_orientation(
series_description)
if scan_orientation:
info_object['ScanOrientation'] = scan_orientation
# Classify Anatomy
classification = common_utils.classify_anatomy(classification,
acquisition,
series_description,
scan_coverage)
# # Contrast
classification['Contrast'] = common_utils.get_contrast_classification(
acquisition.label)
if not classification['Contrast']:
classification['Contrast'] = \
common_utils.get_contrast_classification(
series_description)
if scan_coverage:
spacing_between_slices = scan_coverage / len(df)
info_object['SpacingBetweenSlices'] = round(
spacing_between_slices, 2)
dcm_metadata['info'].update(info_object)
dcm_metadata['classification'] = classification
return dcm_metadata