shrink_factor(s): Number(s) greater than one, such that the new image's size is original_size/shrink_factor. format ( final_errors_mean, final_errors_std, final_errors_max ))ĭef smooth_and_resample ( image, shrink_factors, smoothing_sigmas ): """ Args: image: The image we want to resample. title ( 'TRE histogram' ) print ( 'Initial alignment errors in millimeters, mean(std): '. hist ( final_errors, bins = 20, alpha = 0.5, label = 'after registration', color = 'green' ) plt. hist ( initial_errors, bins = 20, alpha = 0.5, label = 'before registration', color = 'blue' ) plt. registration_errors ( tx, points, points ) plt. Euler3DTransform (), points, points ) final_errors_mean, final_errors_std, _, final_errors_max, final_errors = ru. # Select the fixed and moving images, valid entries are in fixed_image_index = 0 moving_image_index = 7 tx = demons_registration ( fixed_image = images, moving_image = images, fixed_points = points, moving_points = points ) initial_errors_mean, initial_errors_std, _, initial_errors_max, initial_errors = ru. #%%timeit -r1 -n1 # Uncomment the line above if you want to time the running of this cell. metric_and_reference_plot_values ( registration_method, fixed_points, moving_points )) return registration_method. metric_and_reference_end_plot ) registration_method. metric_and_reference_start_plot ) registration_method. if fixed_points and moving_points : registration_method. SetOptimizerScalesFromPhysicalShift () # If corresponding points in the fixed and moving image are given then we display the similarity metric # and the TRE during the registration. SetOptimizerAsGradientDescent ( learningRate = 1.0, numberOfIterations = 20, convergenceMinimumValue = 1e-6, convergenceWindowSize = 10 ) registration_method.
#World of demons pre register code
sitkLinear ) # If you have time, run this code as is, otherwise switch to the gradient descent optimizer #registration_method.SetOptimizerAsConjugateGradientLineSearch(learningRate=1.0, numberOfIterations=20, convergenceMinimumValue=1e-6, convergenceWindowSize=10) registration_method. SetSmoothingSigmasPerLevel ( smoothingSigmas = ) registration_method. SetShrinkFactorsPerLevel ( shrinkFactors = ) registration_method. SetMetricAsDemons ( 10 ) #intensities are equal if the difference is less than 10HU # Multi-resolution framework. SetInitialTransform ( initial_transform ) registration_method. SetSmoothingGaussianOnUpdate ( varianceForUpdateField = 0.0, varianceForTotalField = 2.0 ) registration_method. Transform ())) # Regularization (update field - viscous, total field - elastic). DisplacementFieldTransform ( transform_to_displacment_field_filter. SetReferenceImage ( fixed_image ) # The image returned from the initial_transform_filter is transferred to the transform and cleared out. TransformToDisplacementFieldFilter () transform_to_displacment_field_filter. transform_to_displacment_field_filter = sitk. ImageRegistrationMethod () # Create initial identity transformation. The POPI data, and additional 4D CT data sets with reference points are available from the CREATIS Laboratory here.įetching POPI/masks/00-air-body-lungs.mhdįetching POPI/masks/10-air-body-lungs.mhdįetching POPI/masks/20-air-body-lungs.mhdįetching POPI/masks/30-air-body-lungs.mhdįetching POPI/masks/40-air-body-lungs.mhdįetching POPI/masks/50-air-body-lungs.mhdįetching POPI/masks/60-air-body-lungs.mhdįetching POPI/masks/70-air-body-lungs.mhdįetching POPI/masks/80-air-body-lungs.mhdįetching POPI/masks/90-air-body-lungs.mhdĭef demons_registration ( fixed_image, moving_image, fixed_points = None, moving_points = None ): registration_method = sitk. XVth International Conference on the Use of Computers in Radiation Therapy (ICCR), Toronto, Canada, 2007. Clarysse, "The POPI-model, a point-validated pixel-based breathing thorax model", The POPI model is provided by the Léon Bérard Cancer Center & CREATIS Laboratory, Lyon, France. This data consists of a set of temporal CT volumes, a set of masks segmenting each of the CTs to air/body/lung, and a set of corresponding points across the CT volumes. The data we work with is a 4D (3D+time) thoracic-abdominal CT, the Point-validated Pixel-based Breathing Thorax Model (POPI) model.
#World of demons pre register registration
These include both the DemonsMetric which is part of the registration framework and Demons registration filters which are not. This notebook illustrates the use of the Demons based non-rigid registration set of algorithms in SimpleITK.
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