def test_exception_when_adding_frame_with_wrong_data_type(
self, segmentation_type, dtype):
dataset = SegmentationDataset(rows=1,
columns=1,
segmentation_type=segmentation_type)
with pytest.raises(ValueError, match='.*requires.*?data type'):
dataset.add_frame(np.zeros((1, 1), dtype=dtype), 1)
def test_adding_fractional_frame_modifies_pixel_data(self):
dataset = SegmentationDataset(
rows=2, columns=2, segmentation_type=SegmentationType.FRACTIONAL)
self.setup_dummy_segment(dataset)
assert len(dataset.PixelData) == 0
dataset.add_frame(np.zeros((2, 2), dtype=np.float32), 1)
assert len(dataset.PixelData) == 4
for _ in range(2):
dataset.add_frame(np.ones((2, 2), dtype=np.float32), 1)
assert len(dataset.PixelData) == 12
def test_adding_binary_frame_modifies_pixel_data(self):
dataset = SegmentationDataset(
rows=2, columns=2, segmentation_type=SegmentationType.BINARY)
self.setup_dummy_segment(dataset)
assert len(dataset.PixelData) == 0
dataset.add_frame(np.zeros((2, 2), dtype=np.uint8), 1)
assert len(dataset.PixelData) == 1
for _ in range(2):
dataset.add_frame(np.ones((2, 2), dtype=np.uint8), 1)
assert len(dataset.PixelData) == 2
class TestSegmentationDataset:
def setup(self):
self.dataset = SegmentationDataset(
rows=1, columns=1, segmentation_type=SegmentationType.BINARY)
self.setup_dummy_segment(self.dataset)
def setup_dummy_segment(self, dataset: pydicom.Dataset):
ds = pydicom.Dataset()
ds.SegmentNumber = 1
dataset.SegmentSequence.append(ds)
def generate_dummy_source_image(self):
ds = pydicom.Dataset()
ds.SOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage
ds.SOPInstanceUID = pydicom.uid.generate_uid()
ds.SeriesInstanceUID = pydicom.uid.generate_uid()
return ds
def test_dataset_is_writable(self):
with tempfile.NamedTemporaryFile() as ofile:
self.dataset.save_as(ofile.name)
def test_dataset_has_valid_file_meta(self):
pydicom.dataset.validate_file_meta(self.dataset.file_meta)
def test_file_meta_has_information_group_length_computed(self):
assert 'FileMetaInformationGroupLength' in self.dataset.file_meta
assert self.dataset.file_meta.FileMetaInformationGroupLength > 0
def test_mandatory_sop_common(self):
assert self.dataset.SOPClassUID == '1.2.840.10008.5.1.4.1.1.66.4'
assert 'SOPInstanceUID' in self.dataset
def test_mandatory_enhanced_equipment_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.5.2.html#table_C.7-8b"""
assert self.dataset.Manufacturer == 'pydicom-seg'
assert self.dataset.ManufacturerModelName == 'https://github.com/razorx89/pydicom-seg'
assert self.dataset.DeviceSerialNumber == '0'
assert self.dataset.SoftwareVersions == __version__
def test_mandatory_frame_of_reference_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.4.html#table_C.7-6"""
assert 'FrameOfReferenceUID' in self.dataset
def test_mandatory_gernal_series_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.3.html#table_C.7-5a"""
assert self.dataset.Modality == 'SEG'
assert 'SeriesInstanceUID' in self.dataset
def test_mandatory_segmentation_series_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.html#table_C.8.20-1"""
assert self.dataset.Modality == 'SEG'
assert self.dataset.SeriesNumber
def test_mandatory_image_pixel_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.6.3.html#table_C.7-11a"""
assert self.dataset.SamplesPerPixel >= 1
assert self.dataset.PhotometricInterpretation in [
'MONOCHROME1', 'MONOCHROME2'
]
assert 'Rows' in self.dataset
assert 'Columns' in self.dataset
assert self.dataset.BitsAllocated in [1, 8, 16]
assert 0 < self.dataset.BitsStored <= self.dataset.BitsAllocated
assert self.dataset.HighBit == self.dataset.BitsStored - 1
assert self.dataset.PixelRepresentation in [0, 1]
def test_mandatory_and_common_segmentation_image_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.2.html#table_C.8.20-2"""
assert 'ImageType' in self.dataset
assert all([
a == b
for a, b in zip(self.dataset.ImageType, ['DERIVED', 'PRIMARY'])
])
assert self.dataset.InstanceNumber
assert self.dataset.ContentLabel == 'SEGMENTATION'
assert 'ContentCreatorName' in self.dataset
assert 'ContentDescription' in self.dataset
assert self.dataset.SamplesPerPixel == 1
assert self.dataset.PhotometricInterpretation == 'MONOCHROME2'
assert self.dataset.PixelRepresentation == 0
assert self.dataset.LossyImageCompression == '00'
assert 'SegmentSequence' in self.dataset
def test_mandatory_binary_segmentation_image_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.2.html#table_C.8.20-2"""
assert self.dataset.BitsAllocated == 1
assert self.dataset.BitsStored == 1
assert self.dataset.HighBit == 0
assert self.dataset.SegmentationType == 'BINARY'
@pytest.mark.parametrize('fractional_type', ['PROBABILITY', 'OCCUPANCY'])
def test_mandatory_fractional_segmentation_image_elements(
self, fractional_type):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.2.html#table_C.8.20-2"""
dataset = SegmentationDataset(
rows=1,
columns=1,
segmentation_type=SegmentationType.FRACTIONAL,
segmentation_fractional_type=SegmentationFractionalType(
fractional_type))
assert dataset.BitsAllocated == 8
assert dataset.BitsStored == 8
assert dataset.HighBit == 7 # Little Endian
assert dataset.SegmentationType == 'FRACTIONAL'
assert dataset.SegmentationFractionalType == fractional_type
assert dataset.MaximumFractionalValue == 255
def test_mandatory_multi_frame_functional_groups_elements(self):
"""http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.6.16.html#table_C.7.6.16-1"""
assert 'SharedFunctionalGroupsSequence' in self.dataset
assert len(self.dataset.SharedFunctionalGroupsSequence) == 1
assert 'PerFrameFunctionalGroupsSequence' in self.dataset
assert self.dataset.NumberOfFrames == 0
assert self.dataset.InstanceNumber
assert 'ContentDate' in self.dataset
assert 'ContentTime' in self.dataset
def test_timestamps_exist(self):
assert 'InstanceCreationDate' in self.dataset
assert 'InstanceCreationTime' in self.dataset
assert self.dataset.InstanceCreationDate == self.dataset.SeriesDate
assert self.dataset.InstanceCreationTime == self.dataset.SeriesTime
assert self.dataset.InstanceCreationDate == self.dataset.ContentDate
assert self.dataset.InstanceCreationTime == self.dataset.ContentTime
def test_exception_on_invalid_image_dimensions(self):
with pytest.raises(ValueError, match='.*must be larger than zero'):
SegmentationDataset(rows=0,
columns=0,
segmentation_type=SegmentationType.BINARY)
@pytest.mark.parametrize('max_fractional_value', [-1, 0, 256])
def test_exception_on_invalid_max_fractional_value(self,
max_fractional_value):
with pytest.raises(ValueError,
match='Invalid maximum fractional value.*'):
SegmentationDataset(
rows=1,
columns=1,
segmentation_type=SegmentationType.FRACTIONAL,
max_fractional_value=max_fractional_value,
)
def test_exception_when_adding_frame_with_wrong_rank(self):
with pytest.raises(ValueError, match='.*expecting 2D image'):
self.dataset.add_frame(np.zeros((1, 1, 1), dtype=np.uint8), 1)
def test_exception_when_adding_frame_with_wrong_shape(self):
with pytest.raises(ValueError, match='.*expecting \\d+x\\d+ images'):
self.dataset.add_frame(np.zeros((2, 1), dtype=np.uint8), 1)
@pytest.mark.parametrize('segmentation_type,dtype',
[(SegmentationType.BINARY, np.float32),
(SegmentationType.FRACTIONAL, np.uint8)])
def test_exception_when_adding_frame_with_wrong_data_type(
self, segmentation_type, dtype):
dataset = SegmentationDataset(rows=1,
columns=1,
segmentation_type=segmentation_type)
with pytest.raises(ValueError, match='.*requires.*?data type'):
dataset.add_frame(np.zeros((1, 1), dtype=dtype), 1)
def test_adding_frame_increases_number_of_frames(self):
old_count = self.dataset.NumberOfFrames
print(type(old_count))
self.dataset.add_frame(np.zeros((1, 1), dtype=np.uint8), 1)
assert self.dataset.NumberOfFrames == old_count + 1
def test_adding_binary_frame_modifies_pixel_data(self):
dataset = SegmentationDataset(
rows=2, columns=2, segmentation_type=SegmentationType.BINARY)
self.setup_dummy_segment(dataset)
assert len(dataset.PixelData) == 0
dataset.add_frame(np.zeros((2, 2), dtype=np.uint8), 1)
assert len(dataset.PixelData) == 1
for _ in range(2):
dataset.add_frame(np.ones((2, 2), dtype=np.uint8), 1)
assert len(dataset.PixelData) == 2
def test_adding_fractional_frame_modifies_pixel_data(self):
dataset = SegmentationDataset(
rows=2, columns=2, segmentation_type=SegmentationType.FRACTIONAL)
self.setup_dummy_segment(dataset)
assert len(dataset.PixelData) == 0
dataset.add_frame(np.zeros((2, 2), dtype=np.float32), 1)
assert len(dataset.PixelData) == 4
for _ in range(2):
dataset.add_frame(np.ones((2, 2), dtype=np.float32), 1)
assert len(dataset.PixelData) == 12
def test_adding_frame_with_reference_creates_referenced_series_sequence(
self):
assert 'ReferencedSeriesSequence' not in self.dataset
dummy = self.generate_dummy_source_image()
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1, [dummy])
assert 'ReferencedSeriesSequence' in self.dataset
series_sequence = self.dataset.ReferencedSeriesSequence
assert len(series_sequence) == 1
assert series_sequence[0].SeriesInstanceUID == dummy.SeriesInstanceUID
assert 'ReferencedInstanceSequence' in series_sequence[0]
instance_sequence = series_sequence[0].ReferencedInstanceSequence
assert len(instance_sequence) == 1
assert instance_sequence[0].ReferencedSOPClassUID == dummy.SOPClassUID
assert instance_sequence[
0].ReferencedSOPInstanceUID == dummy.SOPInstanceUID
def test_adding_frames_with_different_references_from_same_series(self):
dummy1 = self.generate_dummy_source_image()
dummy2 = self.generate_dummy_source_image()
dummy2.SeriesInstanceUID = dummy1.SeriesInstanceUID
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1, [dummy1])
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1, [dummy2])
series_sequence = self.dataset.ReferencedSeriesSequence
assert len(series_sequence) == 1
assert series_sequence[0].SeriesInstanceUID == dummy1.SeriesInstanceUID
instance_sequence = series_sequence[0].ReferencedInstanceSequence
assert len(instance_sequence) == 2
assert instance_sequence[
0].ReferencedSOPInstanceUID == dummy1.SOPInstanceUID
assert instance_sequence[
1].ReferencedSOPInstanceUID == dummy2.SOPInstanceUID
def test_adding_frames_with_different_references_from_different_series(
self):
dummies = [self.generate_dummy_source_image() for _ in range(2)]
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1, [dummies[0]])
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1, [dummies[1]])
series_sequence = self.dataset.ReferencedSeriesSequence
assert len(series_sequence) == 2
assert series_sequence[0].SeriesInstanceUID == dummies[
0].SeriesInstanceUID
assert series_sequence[1].SeriesInstanceUID == dummies[
1].SeriesInstanceUID
instance_sequence = series_sequence[0].ReferencedInstanceSequence
assert len(instance_sequence) == 1
assert instance_sequence[0].ReferencedSOPInstanceUID == dummies[
0].SOPInstanceUID
instance_sequence = series_sequence[1].ReferencedInstanceSequence
assert len(instance_sequence) == 1
assert instance_sequence[0].ReferencedSOPInstanceUID == dummies[
1].SOPInstanceUID
def test_adding_instance_reference_multiple_times(self):
dummy = self.generate_dummy_source_image()
item_added = self.dataset.add_instance_reference(dummy)
assert item_added
item_added = self.dataset.add_instance_reference(dummy)
assert not item_added
series_sequence = self.dataset.ReferencedSeriesSequence
assert len(series_sequence) == 1
assert series_sequence[0].SeriesInstanceUID == dummy.SeriesInstanceUID
assert len(series_sequence[0].ReferencedInstanceSequence) == 1
def test_adding_frame_increases_count_of_per_functional_groups_sequence(
self):
assert len(self.dataset.PerFrameFunctionalGroupsSequence) == 0
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1)
assert len(self.dataset.PerFrameFunctionalGroupsSequence) == 1
def test_adding_frame_adds_derivation_image_sequence_to_per_frame_functional_group_item(
self):
frame_item = self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1)
assert 'DerivationImageSequence' in frame_item
dummy = self.generate_dummy_source_image()
frame_item = self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1,
[dummy])
assert 'SourceImageSequence' in frame_item.DerivationImageSequence[0]
assert len(
frame_item.DerivationImageSequence[0].SourceImageSequence) == 1
def test_adding_frame_adds_referenced_segment_to_per_frame_functional_group_item(
self):
frame_item = self.dataset.add_frame(np.zeros((1, 1), np.uint8), 1)
assert 'SegmentIdentificationSequence' in frame_item
assert len(frame_item.SegmentIdentificationSequence) == 1
segment_id_item = frame_item.SegmentIdentificationSequence[0]
assert 'ReferencedSegmentNumber' in segment_id_item
assert segment_id_item.ReferencedSegmentNumber == 1
def test_exception_on_adding_frame_with_non_existing_segment(self):
with pytest.raises(IndexError, match='Segment not found.*'):
self.dataset.add_frame(np.zeros((1, 1), np.uint8), 2)
def test_add_dimension_organization(self):
assert 'DimensionOrganizationSequence' not in self.dataset
assert 'DimensionIndexSequence' not in self.dataset
seq = DimensionOrganizationSequence()
seq.add_dimension('ReferencedSegmentNumber',
'SegmentIdentificationSequence')
seq.add_dimension('ImagePositionPatient', 'PlanePositionSequence')
self.dataset.add_dimension_organization(seq)
assert len(self.dataset.DimensionOrganizationSequence) == 1
assert len(self.dataset.DimensionIndexSequence) == 2
assert self.dataset.DimensionIndexSequence[
0].DimensionDescriptionLabel == 'ReferencedSegmentNumber'
assert self.dataset.DimensionIndexSequence[
1].DimensionDescriptionLabel == 'ImagePositionPatient'
def test_add_dimension_organization_duplicate(self):
seq = DimensionOrganizationSequence()
seq.add_dimension('ReferencedSegmentNumber',
'SegmentIdentificationSequence')
seq.add_dimension('ImagePositionPatient', 'PlanePositionSequence')
self.dataset.add_dimension_organization(seq)
with pytest.raises(ValueError,
match='Dimension organization with UID.*'):
self.dataset.add_dimension_organization(seq)
def test_add_multiple_dimension_organizations(self):
for _ in range(2):
seq = DimensionOrganizationSequence()
seq.add_dimension('ReferencedSegmentNumber',
'SegmentIdentificationSequence')
seq.add_dimension('ImagePositionPatient', 'PlanePositionSequence')
self.dataset.add_dimension_organization(seq)
assert len(self.dataset.DimensionOrganizationSequence) == 2
assert len(self.dataset.DimensionIndexSequence) == 4
def write(self, segmentation: sitk.Image,
source_images: List[pydicom.Dataset]) -> pydicom.Dataset:
"""Writes a DICOM-SEG dataset from a segmentation image and the
corresponding DICOM source images.
Args:
segmentation: A `SimpleITK.Image` with integer labels and a single
component per spatial location.
source_images: A list of `pydicom.Dataset` which are the
source images for the segmentation image.
Returns:
A `pydicom.Dataset` instance with all necessary information and
meta information for writing the dataset to disk.
"""
if segmentation.GetDimension() != 3:
raise ValueError("Only 3D segmentation data is supported")
if segmentation.GetNumberOfComponentsPerPixel() > 1:
raise ValueError("Multi-class segmentations can only be "
"represented with a single component per voxel")
if segmentation.GetPixelID() not in [
sitk.sitkUInt8,
sitk.sitkUInt16,
sitk.sitkUInt32,
sitk.sitkUInt64,
]:
raise ValueError("Unsigned integer data type required")
# TODO Add further checks if source images are from the same series
slice_to_source_images = self._map_source_images_to_segmentation(
segmentation, source_images)
# Compute unique labels and their respective bounding boxes
label_statistics_filter = sitk.LabelStatisticsImageFilter()
label_statistics_filter.Execute(segmentation, segmentation)
unique_labels = set(
[x for x in label_statistics_filter.GetLabels() if x != 0])
if len(unique_labels) == 0:
raise ValueError("Segmentation does not contain any labels")
# Check if all present labels where declared in the DICOM template
declared_segments = set(
[x.SegmentNumber for x in self._template.SegmentSequence])
missing_declarations = unique_labels.difference(declared_segments)
if missing_declarations:
missing_segment_numbers = ", ".join(
[str(x) for x in missing_declarations])
message = (
f"Skipping segment(s) {missing_segment_numbers}, since their "
"declaration is missing in the DICOM template")
if not self._skip_missing_segment:
raise ValueError(message)
logger.warning(message)
labels_to_process = unique_labels.intersection(declared_segments)
if not labels_to_process:
raise ValueError("No segments found for encoding as DICOM-SEG")
# Compute bounding boxes for each present label and optionally restrict
# the volume to serialize to the joined maximum extent
bboxs = {
x: label_statistics_filter.GetBoundingBox(x)
for x in labels_to_process
}
if self._inplane_cropping:
min_x, min_y, _ = np.min([x[::2] for x in bboxs.values()],
axis=0).tolist()
max_x, max_y, _ = (
np.max([x[1::2]
for x in bboxs.values()], axis=0) + 1).tolist()
logger.info(
"Serializing cropped image planes starting at coordinates "
f"({min_x}, {min_y}) with size ({max_x - min_x}, {max_y - min_y})"
)
else:
min_x, min_y = 0, 0
max_x, max_y = segmentation.GetWidth(), segmentation.GetHeight()
logger.info(
f"Serializing image planes at full size ({max_x}, {max_y})")
# Create target dataset for storing serialized data
result = SegmentationDataset(
reference_dicom=source_images[0] if source_images else None,
rows=max_y - min_y,
columns=max_x - min_x,
segmentation_type=SegmentationType.BINARY,
)
dimension_organization = DimensionOrganizationSequence()
dimension_organization.add_dimension("ReferencedSegmentNumber",
"SegmentIdentificationSequence")
dimension_organization.add_dimension("ImagePositionPatient",
"PlanePositionSequence")
result.add_dimension_organization(dimension_organization)
writer_utils.copy_segmentation_template(
target=result,
template=self._template,
segments=labels_to_process,
skip_missing_segment=self._skip_missing_segment,
)
writer_utils.set_shared_functional_groups_sequence(
target=result, segmentation=segmentation)
# FIX - Use ImageOrientationPatient value from DICOM source rather than the segmentation
result.SharedFunctionalGroupsSequence[0].PlaneOrientationSequence[
0].ImageOrientationPatient = source_images[
0].ImageOrientationPatient
buffer = sitk.GetArrayFromImage(segmentation)
for segment in labels_to_process:
logger.info(f"Processing segment {segment}")
if self._skip_empty_slices:
bbox = bboxs[segment]
min_z, max_z = bbox[4], bbox[5] + 1
else:
min_z, max_z = 0, segmentation.GetDepth()
logger.info(
"Total number of slices that will be processed for segment "
f"{segment} is {max_z - min_z} (inclusive from {min_z} to {max_z})"
)
skipped_slices = []
for slice_idx in range(min_z, max_z):
frame_index = (min_x, min_y, slice_idx)
frame_position = segmentation.TransformIndexToPhysicalPoint(
frame_index)
frame_data = np.equal(
buffer[slice_idx, min_y:max_y, min_x:max_x], segment)
if self._skip_empty_slices and not frame_data.any():
skipped_slices.append(slice_idx)
continue
frame_fg_item = result.add_frame(
data=frame_data.astype(np.uint8),
referenced_segment=segment,
referenced_images=slice_to_source_images[slice_idx],
)
frame_fg_item.FrameContentSequence = [pydicom.Dataset()]
frame_fg_item.FrameContentSequence[0].DimensionIndexValues = [
segment, # Segment number
slice_idx - min_z + 1, # Slice index within cropped volume
]
frame_fg_item.PlanePositionSequence = [pydicom.Dataset()]
frame_fg_item.PlanePositionSequence[0].ImagePositionPatient = [
f"{x:e}" for x in frame_position
]
if skipped_slices:
logger.info(f"Skipped empty slices for segment {segment}: "
f'{", ".join([str(x) for x in skipped_slices])}')
# Encode all frames into a bytearray
if self._inplane_cropping or self._skip_empty_slices:
num_encoded_bytes = len(result.PixelData)
max_encoded_bytes = (segmentation.GetWidth() *
segmentation.GetHeight() *
segmentation.GetDepth() *
len(result.SegmentSequence) // 8)
savings = (1 - num_encoded_bytes / max_encoded_bytes) * 100
logger.info(
f"Optimized frame data length is {num_encoded_bytes:,}B "
f"instead of {max_encoded_bytes:,}B (saved {savings:.2f}%)")
result.SegmentsOverlap = "NO"
return result