def test_from_named_components_with_separator_from_bytes(self):
# If the names already include separator chars
# a ValueError should be raised
with pytest.raises(ValueError):
PersonName.from_named_components(
family_name_ideographic=b'\033$B;3ED\033(B^\033$BB@O:\033(B',
encodings=[default_encoding, 'iso2022_jp'],
)
def test_from_named_components(self):
# Example from DICOM standard, part 5, sect 6.2.1.1
pn = PersonName.from_named_components(family_name='Adams',
given_name='John Robert Quincy',
name_prefix='Rev.',
name_suffix='B.A. M.Div.')
assert pn == 'Adams^John Robert Quincy^^Rev.^B.A. M.Div.'
assert pn.family_name == 'Adams'
assert pn.given_name == 'John Robert Quincy'
assert pn.name_prefix == 'Rev.'
assert pn.name_suffix == 'B.A. M.Div.'
def test_from_named_components_jp_from_unicode(self):
# Example name from PS3.5-2008 section H p. 98
pn = PersonName.from_named_components(
family_name='Yamada',
given_name='Tarou',
family_name_ideographic='山田',
given_name_ideographic='太郎',
family_name_phonetic='やまだ',
given_name_phonetic='たろう',
encodings=[default_encoding, 'iso2022_jp'],
)
pn = pn.decode()
assert ("Yamada", "Tarou") == (pn.family_name, pn.given_name)
assert "山田^太郎" == pn.ideographic
assert "やまだ^たろう" == pn.phonetic
def test_from_named_components_jp_from_bytes(self):
# Example name from PS3.5-2008 section H p. 98
pn = PersonName.from_named_components(
family_name='Yamada',
given_name='Tarou',
family_name_ideographic=b'\033$B;3ED\033(B',
given_name_ideographic=b'\033$BB@O:\033(B',
family_name_phonetic=b'\033$B$d$^$@\033(B',
given_name_phonetic=b'\033$B$?$m$&\033(B',
encodings=[default_encoding, 'iso2022_jp'],
)
pn = pn.decode()
assert ("Yamada", "Tarou") == (pn.family_name, pn.given_name)
assert "山田^太郎" == pn.ideographic
assert "やまだ^たろう" == pn.phonetic
def test_from_named_components_kr_from_unicode(self):
# Example name from PS3.5-2008 section I.2 p. 108
pn = PersonName.from_named_components(
family_name='Hong',
given_name='Gildong',
family_name_ideographic='洪',
given_name_ideographic='吉洞',
family_name_phonetic='홍',
given_name_phonetic='길동',
encodings=[default_encoding, 'euc_kr'],
)
pn = pn.decode()
assert ("Hong", "Gildong") == (pn.family_name, pn.given_name)
assert "洪^吉洞" == pn.ideographic
assert "홍^길동" == pn.phonetic
def test_from_named_components_kr_from_bytes(self):
# Example name from PS3.5-2008 section I.2 p. 108
pn = PersonName.from_named_components(
family_name='Hong',
given_name='Gildong',
family_name_ideographic=b'\033$)C\373\363',
given_name_ideographic=b'\033$)C\321\316\324\327',
family_name_phonetic=b'\033$)C\310\253',
given_name_phonetic=b'\033$)C\261\346\265\277',
encodings=[default_encoding, 'euc_kr'],
)
pn = pn.decode()
assert ("Hong", "Gildong") == (pn.family_name, pn.given_name)
assert "洪^吉洞" == pn.ideographic
assert "홍^길동" == pn.phonetic
def get_sr_dataset(
self,
roi_measurements: List[VolumetricROIMeasurementsAndQualitativeEvaluations],
ct_datasets: List[Dataset],
seg_dataset: Segmentation,
series_number: int,
series_description: str
):
"""Construct a Structured Report containing measurements and
evaluations for one or more annotations.
Depending on the command-line parameters, each SR document may contain
measurements and evaluations for the annotation of a single nodule by a
single reader, or in the case of "--composite", measurements and
evaluations of one or more nodules, each annotated by one or more
distinct readers.
Parameters
----------
roi_measurements: List[highdicom.sr.VolumetricROIMeasurementsAndQualitativeEvaluations]
List of measurement groups, each containing to the measurements and
evaluations of a single nodule by a single reader, and referencing
a single segment in a segmentation image.
ct_datasets: List[pydicom.dataset.Dataset]
List of CT datasets from which the segmentations were derived.
seg_dataset: Segmentation
Segmentation image referenced by the SR.
series_number: int
Series number of the newly created SR document.
series_description: str
Series description of the newly-created SR document.
Returns
-------
highdicom.sr.Comprehensive3DSR
Structured Report document dataset using template TID 1500
containing measurements and evaluations for one or more nodules.
"""
# Be explicit about reader being anonymous
anonymous_person_name = PersonName.from_named_components(
given_name='anonymous',
family_name='observer'
)
observer_context = ObserverContext(
observer_type=codes.DCM.Person,
observer_identifying_attributes=PersonObserverIdentifyingAttributes(
name=anonymous_person_name
)
)
observation_context = ObservationContext(
observer_person_context=observer_context
)
measurement_report = MeasurementReport(
observation_context=observation_context,
procedure_reported=codes.LN.CTUnspecifiedBodyRegion,
imaging_measurements=roi_measurements
)
# Create the Structured Report instance
sr_dcm = Comprehensive3DSR(
evidence=ct_datasets + [seg_dataset],
content=measurement_report[0],
series_number=series_number,
series_instance_uid=UID(),
sop_instance_uid=UID(),
instance_number=1,
manufacturer='highdicom developers',
is_complete=True,
is_verified=True,
verifying_observer_name=anonymous_person_name,
verifying_organization='anonymous',
series_description=series_description
)
return sr_dcm
def get_segmentation_dataset(
self,
ct_datasets: List[Dataset],
pixel_array: np.ndarray,
seg_descs: List[SegmentDescription],
series_number: int,
series_description: str
):
"""Construct the SOP Instance dataset for a segmentation image.
This method includes information common to all segmentation images,
such as the manufacturer name and software version.
Parameters
----------
ct_datasets: List[pydicom.dataset.Dataset]
List of CT datasets from which the segmentations were derived.
pixel_array: np.ndarray
Pixel array of the segmentation masks
seg_descs: List[SegmentDescription]
Descriptions of each segment in the segmentation image.
series_number: int
Series number to apply to the newly created segmentation image.
series_description: str
Series description of the newly-created segmentation image.
Returns
-------
highdicom.seg.Segmentation
Dataset for the newly-created Segmentation SOP Instance.
"""
anonymous_person_name = PersonName.from_named_components(
given_name='anonymous',
family_name='observer'
)
seg_dcm = Segmentation(
source_images=ct_datasets,
pixel_array=pixel_array,
segmentation_type=SegmentationTypeValues.BINARY,
segment_descriptions=seg_descs,
series_instance_uid=UID(),
series_number=series_number,
sop_instance_uid=UID(),
instance_number=1,
manufacturer="highdicom developers",
manufacturer_model_name="highdicom",
software_versions=f"{highdicom_version}",
device_serial_number='1',
content_description="Lung nodule segmentation",
content_creator_name=anonymous_person_name,
series_description=series_description
)
# Add in some extra information
seg_dcm.BodyPartExamined = "LUNG"
seg_dcm.ClinicalTrialSeriesID = "Session1"
seg_dcm.ClinicalTrialTimePointID = "1"
seg_dcm.ClinicalTrialCoordinatingCenterName = "TCIA"
seg_dcm.ContentLabel = "SEGMENTATION"
return seg_dcm
def test_from_named_components_with_separator(self):
# If the names already include separator chars
# a ValueError should be raised
with pytest.raises(ValueError):
PersonName.from_named_components(given_name='Yamada^Tarou')
