def cardiac_service(data_objects, working_dir, settings):
"""
Implements the platipy framework to provide cardiac atlas based segmentation.
"""
logger.info("Running Cardiac Segmentation")
logger.info("Using settings: " + str(settings))
output_objects = []
for data_object in data_objects:
logger.info("Running on data object: " + data_object.path)
# Read the image series
load_path = data_object.path
if data_object.type == "DICOM":
load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(
data_object.path)
img = sitk.ReadImage(load_path)
results = run_cardiac_segmentation(img, settings)
# Save resulting masks and add to output for service
for output in results.keys():
mask_file = os.path.join(working_dir, "{0}.nii.gz".format(output))
sitk.WriteImage(results[output], mask_file)
output_data_object = DataObject(type="FILE",
path=mask_file,
parent=data_object)
output_objects.append(output_data_object)
# If the input was a DICOM, then we can use it to generate an output RTStruct
# if data_object.type == 'DICOM':
# dicom_file = load_path[0]
# logger.info('Will write Dicom using file: {0}'.format(dicom_file))
# masks = {settings['outputContourName']: mask_file}
# # Use the image series UID for the file of the RTStruct
# suid = pydicom.dcmread(dicom_file).SeriesInstanceUID
# output_file = os.path.join(working_dir, 'RS.{0}.dcm'.format(suid))
# # Use the convert nifti function to generate RTStruct from nifti masks
# convert_nifti(dicom_file, masks, output_file)
# # Create the Data Object for the RTStruct and add it to the list
# do = DataObject(type='DICOM', path=output_file, parent=d)
# output_objects.append(do)
# logger.info('RTStruct generated')
return output_objects
def cardiac_structure_guided_service(data_objects, working_dir, settings):
"""Runs the structure guided cardiac segmentation service"""
logger.info("Running Structure Guided Cardiac Segmentation")
logger.info("Using settings: " + str(settings))
output_objects = []
for data_object in data_objects:
logger.info("Running on data object: " + data_object.path)
# Read the image series
load_path = data_object.path
if data_object.type == "DICOM":
load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(
data_object.path)
img = sitk.ReadImage(load_path)
# Load the WHOLEHEART contour (child of Image)
if len(data_object.children) == 0:
logger.error(
"Wholeheart structure needed for structure guided cardiac "
f"segmentation, skipping {data_object.id}")
continue
wholeheart = sitk.ReadImage(data_object.children[0].path)
results, _ = run_cardiac_segmentation(img, wholeheart, settings)
# Save resulting masks and add to output for service
for output in results:
mask_file = os.path.join(working_dir, "{0}.nii.gz".format(output))
sitk.WriteImage(results[output], mask_file)
output_data_object = DataObject(type="FILE",
path=mask_file,
parent=data_object)
output_objects.append(output_data_object)
return output_objects
def test_cardiac_service(cardiac_data):
"""An end-to-end test to check that the cardiac service is running as expected"""
with tempfile.TemporaryDirectory() as working_dir:
working_path = Path(working_dir)
# Save off data
cases = list(cardiac_data.keys())
for case in cardiac_data:
ct_path = working_path.joinpath(f"Case_{case}", "Images",
f"Case_{case}_CROP.nii.gz")
ct_path.parent.mkdir(parents=True, exist_ok=True)
mask_path = working_path.joinpath(
f"Case_{case}", "Structures",
f"Case_{case}_WHOLEHEART_CROP.nii.gz")
mask_path.parent.mkdir(parents=True, exist_ok=True)
sitk.WriteImage(cardiac_data[case]["CT"], str(ct_path))
sitk.WriteImage(cardiac_data[case]["WHOLEHEART"], str(mask_path))
# Prepare algorithm settings
test_settings = CARDIAC_SETTINGS_DEFAULTS
test_settings["atlas_settings"]["atlas_id_list"] = cases[:-1]
test_settings["atlas_settings"]["atlas_path"] = str(working_path)
test_settings["atlas_settings"]["atlas_structure_list"] = [
"WHOLEHEART"
]
test_settings["atlas_settings"]["auto_crop_atlas"] = False
test_settings["deformable_registration_settings"][
"resolution_staging"] = [8, 4, 2]
test_settings["deformable_registration_settings"][
"iteration_staging"] = [5, 5, 5]
test_settings["deformable_registration_settings"][
"smoothing_sigmas"] = [0, 0, 0]
test_settings["deformable_registration_settings"][
"default_value"] = -1000
test_settings["iar_settings"]["reference_structure"] = None
test_settings["label_fusion_settings"]["optimal_threshold"] = {
"WHOLEHEART": 0.5
}
test_settings["vessel_spline_settings"]["vessel_name_list"] = []
test_settings["vessel_spline_settings"]["vessel_radius_mm_dict"] = {}
test_settings["vessel_spline_settings"]["scan_direction_dict"] = {}
test_settings["vessel_spline_settings"][
"stop_condition_type_dict"] = {}
test_settings["vessel_spline_settings"][
"stop_condition_value_dict"] = {}
test_settings["postprocessing_settings"]["run_postprocessing"] = False
test_settings["geometric_segmentation_settings"][
"run_geometric_algorithms"] = False
test_settings["rigid_settings"] = {
"shrink_factors": [2, 1],
"smooth_sigmas": [0, 0],
"sampling_rate": 0.75,
"default_value": -1024,
"number_of_iterations": 5,
"final_interp": sitk.sitkBSpline,
"metric": "mean_squares",
"optimiser": "gradient_descent_line_search",
}
# Run the service function.
infer_case = cases[-1]
output, _ = run_cardiac_segmentation(cardiac_data[infer_case]["CT"],
settings=test_settings)
# Check we have a WHOLEHEART structure
assert "WHOLEHEART" in output
# Check the result is similar to the GT
label_overlap_filter = sitk.LabelOverlapMeasuresImageFilter()
auto_mask = output["WHOLEHEART"]
gt_mask = sitk.Cast(cardiac_data[infer_case]["WHOLEHEART"],
auto_mask.GetPixelID())
label_overlap_filter.Execute(auto_mask, gt_mask)
assert label_overlap_filter.GetDiceCoefficient() > 0.99