def main(): time_start = ph.start_timing() np.set_printoptions(precision=3) input_parser = InputArgparser( description="Run reconstruction pipeline including " "(i) bias field correction, " "(ii) volumetric reconstruction in subject space, " "(iii) volumetric reconstruction in template space, " "and (iv) some diagnostics to assess the obtained reconstruction.", ) input_parser.add_filenames(required=True) input_parser.add_filenames_masks(required=True) input_parser.add_target_stack(required=False) input_parser.add_suffix_mask(default="") input_parser.add_dir_output(required=True) input_parser.add_alpha(default=0.01) input_parser.add_verbose(default=0) input_parser.add_gestational_age(required=False) input_parser.add_prefix_output(default="") input_parser.add_search_angle(default=180) input_parser.add_multiresolution(default=0) input_parser.add_log_config(default=1) input_parser.add_isotropic_resolution() input_parser.add_reference() input_parser.add_reference_mask() input_parser.add_bias_field_correction(default=1) input_parser.add_intensity_correction(default=1) input_parser.add_iter_max(default=10) input_parser.add_two_step_cycles(default=3) input_parser.add_slice_thicknesses(default=None) input_parser.add_option( option_string="--run-bias-field-correction", type=int, help="Turn on/off bias field correction. " "If off, it is assumed that this step was already performed " "if --bias-field-correction is active.", default=1) input_parser.add_option( option_string="--run-recon-subject-space", type=int, help="Turn on/off reconstruction in subject space. " "If off, it is assumed that this step was already performed.", default=1) input_parser.add_option( option_string="--run-recon-template-space", type=int, help="Turn on/off reconstruction in template space. " "If off, it is assumed that this step was already performed.", default=1) input_parser.add_option( option_string="--run-diagnostics", type=int, help="Turn on/off diagnostics of the obtained volumetric " "reconstruction. ", default=0) input_parser.add_option( option_string="--initial-transform", type=str, help="Set initial transform to be used for register_image.", default=None) input_parser.add_outlier_rejection(default=1) input_parser.add_threshold_first(default=0.5) input_parser.add_threshold(default=0.8) input_parser.add_argument( "--sda", "-sda", action='store_true', help="If given, the volume is reconstructed using " "Scattered Data Approximation (Vercauteren et al., 2006). " "--alpha is considered the value for the standard deviation then. " "Recommended value is, e.g., --alpha 0.8") input_parser.add_argument( "--v2v-robust", "-v2v-robust", action='store_true', help="If given, a more robust volume-to-volume registration step is " "performed, i.e. four rigid registrations are performed using four " "rigid transform initializations based on " "principal component alignment of associated masks.") input_parser.add_interleave(default=3) input_parser.add_argument( "--s2v-hierarchical", "-s2v-hierarchical", action='store_true', help="If given, a hierarchical approach for the first slice-to-volume " "registration cycle is used, i.e. sub-packages defined by the " "specified interleave (--interleave) are registered until each " "slice is registered independently.") args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) filename_srr = "srr" dir_output_preprocessing = os.path.join(args.dir_output, "preprocessing_n4itk") dir_output_recon_subject_space = os.path.join(args.dir_output, "recon_subject_space") dir_output_recon_template_space = os.path.join(args.dir_output, "recon_template_space") dir_output_diagnostics = os.path.join(args.dir_output, "diagnostics") srr_subject = os.path.join(dir_output_recon_subject_space, "%s_subject.nii.gz" % filename_srr) srr_subject_mask = ph.append_to_filename(srr_subject, "_mask") srr_template = os.path.join(dir_output_recon_template_space, "%s_template.nii.gz" % filename_srr) srr_template_mask = ph.append_to_filename(srr_template, "_mask") trafo_template = os.path.join(dir_output_recon_template_space, "registration_transform_sitk.txt") srr_slice_coverage = os.path.join( dir_output_diagnostics, "%s_template_slicecoverage.nii.gz" % filename_srr) if args.bias_field_correction and args.run_bias_field_correction: for i, f in enumerate(args.filenames): output = os.path.join(dir_output_preprocessing, os.path.basename(f)) cmd_args = [] cmd_args.append("--filename '%s'" % f) cmd_args.append("--filename-mask '%s'" % args.filenames_masks[i]) cmd_args.append("--output '%s'" % output) # cmd_args.append("--verbose %d" % args.verbose) cmd_args.append("--log-config %d" % args.log_config) cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args) time_start_bias = ph.start_timing() exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Bias field correction failed") elapsed_time_bias = ph.stop_timing(time_start_bias) filenames = [ os.path.join(dir_output_preprocessing, os.path.basename(f)) for f in args.filenames ] elif args.bias_field_correction and not args.run_bias_field_correction: elapsed_time_bias = ph.get_zero_time() filenames = [ os.path.join(dir_output_preprocessing, os.path.basename(f)) for f in args.filenames ] else: elapsed_time_bias = ph.get_zero_time() filenames = args.filenames # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack = filenames[0] else: try: target_stack_index = args.filenames.index(args.target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") target_stack = filenames[target_stack_index] # Add single quotes around individual filenames to account for whitespaces filenames = ["'" + f + "'" for f in filenames] filenames_masks = ["'" + f + "'" for f in args.filenames_masks] if args.run_recon_subject_space: cmd_args = ["niftymic_reconstruct_volume"] cmd_args.append("--filenames %s" % (" ").join(filenames)) cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks)) cmd_args.append("--multiresolution %d" % args.multiresolution) cmd_args.append("--target-stack '%s'" % target_stack) cmd_args.append("--output '%s'" % srr_subject) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--intensity-correction %d" % args.intensity_correction) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--two-step-cycles %d" % args.two_step_cycles) cmd_args.append("--outlier-rejection %d" % args.outlier_rejection) cmd_args.append("--threshold-first %f" % args.threshold_first) cmd_args.append("--threshold %f" % args.threshold) if args.slice_thicknesses is not None: cmd_args.append("--slice-thicknesses %s" % " ".join(map(str, args.slice_thicknesses))) cmd_args.append("--verbose %d" % args.verbose) cmd_args.append("--log-config %d" % args.log_config) if args.isotropic_resolution is not None: cmd_args.append("--isotropic-resolution %f" % args.isotropic_resolution) if args.reference is not None: cmd_args.append("--reference %s" % args.reference) if args.reference_mask is not None: cmd_args.append("--reference-mask %s" % args.reference_mask) if args.sda: cmd_args.append("--sda") if args.v2v_robust: cmd_args.append("--v2v-robust") if args.s2v_hierarchical: cmd_args.append("--s2v-hierarchical") cmd = (" ").join(cmd_args) time_start_volrec = ph.start_timing() exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in subject space failed") # Compute SRR mask in subject space # (Approximated using SDA within reconstruct_volume) if 0: dir_motion_correction = os.path.join( dir_output_recon_subject_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % " ".join(filenames_masks)) cmd_args.append("--dir-input-mc '%s'" % dir_motion_correction) cmd_args.append("--output '%s'" % srr_subject_mask) cmd_args.append("--reconstruction-space '%s'" % srr_subject) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--mask") cmd_args.append("--log-config %d" % args.log_config) if args.slice_thicknesses is not None: cmd_args.append("--slice-thicknesses %s" % " ".join(map(str, args.slice_thicknesses))) if args.sda: cmd_args.append("--sda") cmd_args.append("--alpha 1") else: cmd_args.append("--alpha 0.1") cmd_args.append("--iter-max 5") cmd = (" ").join(cmd_args) ph.execute_command(cmd) elapsed_time_volrec = ph.stop_timing(time_start_volrec) else: elapsed_time_volrec = ph.get_zero_time() if args.run_recon_template_space: if args.gestational_age is None: template_stack_estimator = \ tse.TemplateStackEstimator.from_mask(srr_subject_mask) gestational_age = template_stack_estimator.get_estimated_gw() ph.print_info("Estimated gestational age: %d" % gestational_age) else: gestational_age = args.gestational_age template = os.path.join(DIR_TEMPLATES, "STA%d.nii.gz" % gestational_age) template_mask = os.path.join(DIR_TEMPLATES, "STA%d_mask.nii.gz" % gestational_age) # Register SRR to template space cmd_args = ["niftymic_register_image"] cmd_args.append("--fixed '%s'" % template) cmd_args.append("--moving '%s'" % srr_subject) cmd_args.append("--fixed-mask '%s'" % template_mask) cmd_args.append("--moving-mask '%s'" % srr_subject_mask) cmd_args.append( "--dir-input-mc '%s'" % os.path.join(dir_output_recon_subject_space, "motion_correction")) cmd_args.append("--output '%s'" % trafo_template) cmd_args.append("--verbose %s" % args.verbose) cmd_args.append("--log-config %d" % args.log_config) cmd_args.append("--refine-pca") if args.initial_transform is not None: cmd_args.append("--initial-transform '%s'" % args.initial_transform) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Registration to template space failed") # Compute SRR in template space dir_input_mc = os.path.join(dir_output_recon_template_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % (" ").join(filenames)) cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks)) cmd_args.append("--dir-input-mc '%s'" % dir_input_mc) cmd_args.append("--output '%s'" % srr_template) cmd_args.append("--reconstruction-space '%s'" % template) cmd_args.append("--target-stack '%s'" % target_stack) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--verbose %s" % args.verbose) cmd_args.append("--log-config %d" % args.log_config) if args.slice_thicknesses is not None: cmd_args.append("--slice-thicknesses %s" % " ".join(map(str, args.slice_thicknesses))) if args.sda: cmd_args.append("--sda") cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in template space failed") # Compute SRR mask in template space if 1: dir_motion_correction = os.path.join( dir_output_recon_template_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % " ".join(filenames_masks)) cmd_args.append("--dir-input-mc '%s'" % dir_motion_correction) cmd_args.append("--output '%s'" % srr_template_mask) cmd_args.append("--reconstruction-space '%s'" % srr_template) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--log-config %d" % args.log_config) cmd_args.append("--mask") if args.slice_thicknesses is not None: cmd_args.append("--slice-thicknesses %s" % " ".join(map(str, args.slice_thicknesses))) if args.sda: cmd_args.append("--sda") cmd_args.append("--alpha 1") else: cmd_args.append("--alpha 0.1") cmd_args.append("--iter-max 5") cmd = (" ").join(cmd_args) ph.execute_command(cmd) # Copy SRR to output directory if 0: output = "%sSRR_Stacks%d.nii.gz" % (args.prefix_output, len(args.filenames)) path_to_output = os.path.join(args.dir_output, output) cmd = "cp -p '%s' '%s'" % (srr_template, path_to_output) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Copy of SRR to output directory failed") # Multiply template mask with reconstruction if 0: cmd_args = ["niftymic_multiply"] fnames = [ srr_template, srr_template_mask, ] output_masked = "Masked_%s" % output path_to_output_masked = os.path.join(args.dir_output, output_masked) cmd_args.append("--filenames %s" % " ".join(fnames)) cmd_args.append("--output '%s'" % path_to_output_masked) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR brain masking failed") else: elapsed_time_template = ph.get_zero_time() if args.run_diagnostics: dir_input_mc = os.path.join(dir_output_recon_template_space, "motion_correction") dir_output_orig_vs_proj = os.path.join(dir_output_diagnostics, "original_vs_projected") dir_output_selfsimilarity = os.path.join(dir_output_diagnostics, "selfsimilarity") dir_output_orig_vs_proj_pdf = os.path.join(dir_output_orig_vs_proj, "pdf") # Show slice coverage over reconstruction space exe = os.path.abspath(show_slice_coverage.__file__) cmd_args = ["python %s" % exe] cmd_args.append("--filenames %s" % (" ").join(filenames)) cmd_args.append("--dir-input-mc '%s'" % dir_input_mc) cmd_args.append("--reconstruction-space '%s'" % srr_template) cmd_args.append("--output '%s'" % srr_slice_coverage) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Slice coverage visualization failed") # Get simulated/projected slices exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__) cmd_args = ["python %s" % exe] cmd_args.append("--filenames %s" % (" ").join(filenames)) if args.filenames_masks is not None: cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks)) cmd_args.append("--dir-input-mc '%s'" % dir_input_mc) cmd_args.append("--dir-output '%s'" % dir_output_orig_vs_proj) cmd_args.append("--reconstruction '%s'" % srr_template) cmd_args.append("--copy-data 1") if args.slice_thicknesses is not None: cmd_args.append("--slice-thicknesses %s" % " ".join(map(str, args.slice_thicknesses))) # cmd_args.append("--verbose %s" % args.verbose) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR slice projections failed") filenames_simulated = [ "'%s" % os.path.join(dir_output_orig_vs_proj, os.path.basename(f)) for f in filenames ] # Evaluate slice similarities to ground truth exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__) cmd_args = ["python %s" % exe] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) if args.filenames_masks is not None: cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks)) cmd_args.append("--measures NCC SSIM") cmd_args.append("--dir-output '%s'" % dir_output_selfsimilarity) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Evaluation of stack similarities failed") # Generate figures showing the quantitative comparison exe = os.path.abspath( show_evaluated_simulated_stack_similarity.__file__) cmd_args = ["python %s" % exe] cmd_args.append("--dir-input '%s'" % dir_output_selfsimilarity) cmd_args.append("--dir-output '%s'" % dir_output_selfsimilarity) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: ph.print_warning("Visualization of stack similarities failed") # Generate pdfs showing all the side-by-side comparisons if 0: exe = os.path.abspath( export_side_by_side_simulated_vs_original_slice_comparison. __file__) cmd_args = ["python %s" % exe] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) cmd_args.append("--dir-output '%s'" % dir_output_orig_vs_proj_pdf) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Generation of PDF overview failed") ph.print_title("Summary") print("Computational Time for Bias Field Correction: %s" % elapsed_time_bias) print("Computational Time for Volumetric Reconstruction: %s" % elapsed_time_volrec) print("Computational Time for Pipeline: %s" % ph.stop_timing(time_start)) return 0
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Script to study reconstruction parameters and their " "impact on the volumetric reconstruction quality. " "This script can only be used to sweep through one single parameter, " "e.g. the regularization parameter 'alpha'. ") input_parser.add_filenames(required=True) input_parser.add_filenames_masks() input_parser.add_suffix_mask(default="_mask") input_parser.add_dir_input_mc() input_parser.add_dir_output(required=True) input_parser.add_reconstruction_space() input_parser.add_reference( help="Path to reference NIfTI image file. If given the volumetric " "reconstructed is performed in this physical space. " "Either a reconstruction space or a reference must be provided", required=False) input_parser.add_reference_mask(default=None) input_parser.add_study_name() input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_measures( default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_tv_solver(default="PD") input_parser.add_iterations(default=50) input_parser.add_rho(default=0.1) input_parser.add_iter_max(default=10) input_parser.add_minimizer(default="lsmr") input_parser.add_log_config(default=1) input_parser.add_use_masks_srr(default=0) input_parser.add_verbose(default=1) input_parser.add_slice_thicknesses(default=None) input_parser.add_argument( "--append", "-append", action='store_true', help="If given, results are appended to previously executed parameter " "study with identical parameters and study name store in the output " "directory.") # Range for parameter sweeps input_parser.add_alphas(default=list(np.linspace(0.01, 0.5, 5))) input_parser.add_data_losses(default=["linear"] # default=["linear", "arctan"] ) input_parser.add_data_loss_scales(default=[1] # default=[0.1, 0.5, 1.5] ) args = input_parser.parse_args() input_parser.print_arguments(args) if args.reference is None and args.reconstruction_space is None: raise IOError("Either reference (--reference) or reconstruction space " "(--reconstruction-space) must be provided.") if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if args.reference is not None: reference = st.Stack.from_filename(file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack(reference.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = sitk.GetArrayFromImage(reference.sitk).flatten() x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten() else: reconstruction_space = st.Stack.from_filename( file_path=args.reconstruction_space, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack( reconstruction_space.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = None x_ref_mask = None # ----------------------------Set Up Parameters---------------------------- parameters = {} parameters["alpha"] = args.alphas if len(args.data_losses) > 1: parameters["data_loss"] = args.data_losses if len(args.data_loss_scales) > 1: parameters["data_loss_scale"] = args.data_loss_scales # --------------------------Set Up Parameter Study------------------------- ph.print_title("Run Parameter Study") if args.study_name is None: name = args.reconstruction_type else: name = args.study_name reconstruction_info = { "shape": reconstruction_space.sitk.GetSize()[::-1], "origin": reconstruction_space.sitk.GetOrigin(), "spacing": reconstruction_space.sitk.GetSpacing(), "direction": reconstruction_space.sitk.GetDirection(), } # Create Tikhonov solver from which all information can be extracted # (also for other reconstruction types) tmp = tk.TikhonovSolver( stacks=stacks, reconstruction=reconstruction_space, alpha=args.alphas[0], iter_max=args.iter_max, data_loss=args.data_losses[0], data_loss_scale=args.data_loss_scales[0], reg_type="TK1", minimizer=args.minimizer, verbose=args.verbose, use_masks=args.use_masks_srr, ) solver = tmp.get_solver() parameter_study_interface = \ deconv_interface.DeconvolutionParameterStudyInterface( A=solver.get_A(), A_adj=solver.get_A_adj(), D=solver.get_B(), D_adj=solver.get_B_adj(), b=solver.get_b(), x0=solver.get_x0(), alpha=solver.get_alpha(), x_scale=solver.get_x_scale(), data_loss=solver.get_data_loss(), data_loss_scale=solver.get_data_loss_scale(), iter_max=solver.get_iter_max(), minimizer=solver.get_minimizer(), iterations=args.iterations, measures=args.measures, dimension=3, L2=16. / reconstruction_space.sitk.GetSpacing()[0]**2, reconstruction_type=args.reconstruction_type, rho=args.rho, dir_output=args.dir_output, parameters=parameters, name=name, reconstruction_info=reconstruction_info, x_ref=x_ref, x_ref_mask=x_ref_mask, tv_solver=args.tv_solver, verbose=args.verbose, append=args.append, ) parameter_study_interface.set_up_parameter_study() parameter_study = parameter_study_interface.get_parameter_study() # Run parameter study parameter_study.run() print("\nComputational time for Deconvolution Parameter Study %s: %s" % (name, parameter_study.get_computational_time())) return 0
def main(): time_start = ph.start_timing() np.set_printoptions(precision=3) input_parser = InputArgparser( description="Perform (linear) intensity correction across " "stacks/images given a reference stack/image", ) input_parser.add_filenames(required=True) input_parser.add_dir_output(required=True) input_parser.add_reference(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_search_angle(default=180) input_parser.add_prefix_output(default="IC_") input_parser.add_log_config(default=1) input_parser.add_option( option_string="--registration", type=int, help="Turn on/off registration from image to reference prior to " "intensity correction.", default=0) input_parser.add_verbose(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) if args.reference in args.filenames: args.filenames.remove(args.reference) # Read data data_reader = dr.MultipleImagesReader(args.filenames, suffix_mask=args.suffix_mask, extract_slices=False) data_reader.read_data() stacks = data_reader.get_data() data_reader = dr.MultipleImagesReader([args.reference], suffix_mask=args.suffix_mask, extract_slices=False) data_reader.read_data() reference = data_reader.get_data()[0] if args.registration: # Define search angle ranges for FLIRT in all three dimensions search_angles = [ "-searchr%s -%d %d" % (x, args.search_angle, args.search_angle) for x in ["x", "y", "z"] ] search_angles = (" ").join(search_angles) registration = regflirt.FLIRT( moving=reference, registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, options=search_angles, use_verbose=False, ) # Perform Intensity Correction ph.print_title("Perform Intensity Correction") intensity_corrector = ic.IntensityCorrection( use_reference_mask=True, use_individual_slice_correction=False, prefix_corrected=args.prefix_output, use_verbose=False, ) stacks_corrected = [None] * len(stacks) for i, stack in enumerate(stacks): if args.registration: ph.print_info("Image %d/%d: Registration ... " % (i + 1, len(stacks)), newline=False) registration.set_fixed(stack) registration.run() transform_sitk = registration.get_registration_transform_sitk() stack.update_motion_correction(transform_sitk) print("done") ph.print_info("Image %d/%d: Intensity Correction ... " % (i + 1, len(stacks)), newline=False) ref = reference.get_resampled_stack(stack.sitk) ref = st.Stack.from_sitk_image(image_sitk=ref.sitk, image_sitk_mask=stack.sitk_mask * ref.sitk_mask, filename=reference.get_filename()) intensity_corrector.set_stack(stack) intensity_corrector.set_reference(ref) intensity_corrector.run_linear_intensity_correction() # intensity_corrector.run_affine_intensity_correction() stacks_corrected[i] = \ intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # Write Data stacks_corrected[i].write(args.dir_output, write_mask=True, suffix_mask=args.suffix_mask) if args.verbose: sitkh.show_stacks( [ reference, stacks_corrected[i], # stacks[i], ], segmentation=stacks_corrected[i]) # ph.pause() # Write reference too (although not intensity corrected) reference.write(args.dir_output, filename=args.prefix_output + reference.get_filename(), write_mask=True, suffix_mask=args.suffix_mask) elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Intensity Correction(s): %s" % (elapsed_time)) return 0
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) input_parser = InputArgparser( description="Volumetric MRI reconstruction framework to reconstruct " "an isotropic, high-resolution 3D volume from multiple stacks of 2D " "slices with motion correction. The resolution of the computed " "Super-Resolution Reconstruction (SRR) is given by the in-plane " "spacing of the selected target stack. A region of interest can be " "specified by providing a mask for the selected target stack. Only " "this region will then be reconstructed by the SRR algorithm which " "can substantially reduce the computational time.", ) input_parser.add_filenames(required=True) input_parser.add_filenames_masks() input_parser.add_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack(default=None) input_parser.add_search_angle(default=45) input_parser.add_multiresolution(default=0) input_parser.add_shrink_factors(default=[3, 2, 1]) input_parser.add_smoothing_sigmas(default=[1.5, 1, 0]) input_parser.add_sigma(default=1) input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_iterations(default=15) input_parser.add_alpha(default=0.015) input_parser.add_alpha_first(default=0.2) input_parser.add_iter_max(default=10) input_parser.add_iter_max_first(default=5) input_parser.add_dilation_radius(default=3) input_parser.add_extra_frame_target(default=10) input_parser.add_bias_field_correction(default=0) input_parser.add_intensity_correction(default=1) input_parser.add_isotropic_resolution(default=1) input_parser.add_log_config(default=1) input_parser.add_subfolder_motion_correction() input_parser.add_write_motion_correction(default=1) input_parser.add_verbose(default=0) input_parser.add_two_step_cycles(default=3) input_parser.add_use_masks_srr(default=0) input_parser.add_boundary_stacks(default=[10, 10, 0]) input_parser.add_metric(default="Correlation") input_parser.add_metric_radius(default=10) input_parser.add_reference() input_parser.add_reference_mask() input_parser.add_outlier_rejection(default=1) input_parser.add_threshold_first(default=0.5) input_parser.add_threshold(default=0.8) input_parser.add_interleave(default=3) input_parser.add_slice_thicknesses(default=None) input_parser.add_viewer(default="itksnap") input_parser.add_v2v_method(default="RegAladin") input_parser.add_argument( "--v2v-robust", "-v2v-robust", action='store_true', help="If given, a more robust volume-to-volume registration step is " "performed, i.e. four rigid registrations are performed using four " "rigid transform initializations based on " "principal component alignment of associated masks." ) input_parser.add_argument( "--s2v-hierarchical", "-s2v-hierarchical", action='store_true', help="If given, a hierarchical approach for the first slice-to-volume " "registration cycle is used, i.e. sub-packages defined by the " "specified interleave (--interleave) are registered until each " "slice is registered independently." ) input_parser.add_argument( "--sda", "-sda", action='store_true', help="If given, the volumetric reconstructions are performed using " "Scattered Data Approximation (Vercauteren et al., 2006). " "'alpha' is considered the final 'sigma' for the " "iterative adjustment. " "Recommended value is, e.g., --alpha 0.8" ) input_parser.add_option( option_string="--transforms-history", type=int, help="Write entire history of applied slice motion correction " "transformations to motion correction output directory", default=0, ) args = input_parser.parse_args() input_parser.print_arguments(args) rejection_measure = "NCC" threshold_v2v = -2 # 0.3 debug = False if args.v2v_method not in V2V_METHOD_OPTIONS: raise ValueError("v2v-method must be in {%s}" % ( ", ".join(V2V_METHOD_OPTIONS))) if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]): raise ValueError( "output filename invalid; allowed extensions are: %s" % ", ".join(ALLOWED_EXTENSIONS)) if args.alpha_first < args.alpha and not args.sda: raise ValueError("It must hold alpha-first >= alpha") if args.threshold_first > args.threshold: raise ValueError("It must hold threshold-first <= threshold") dir_output = os.path.dirname(args.output) ph.create_directory(dir_output) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, stacks_slice_thicknesses=args.slice_thicknesses, ) if len(args.boundary_stacks) is not 3: raise IOError( "Provide exactly three values for '--boundary-stacks' to define " "cropping in i-, j-, and k-dimension of the input stacks") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if all(s.is_unity_mask() is True for s in stacks): ph.print_warning("No mask is provided! " "Generated reconstruction space may be very big!") ph.print_warning("Consider using a mask to speed up computations") # args.extra_frame_target = 0 # ph.wrint_warning("Overwritten: extra-frame-target set to 0") # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack_index = 0 else: try: target_stack_index = args.filenames.index(args.target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") # ---------------------------Data Preprocessing--------------------------- ph.print_title("Data Preprocessing") segmentation_propagator = segprop.SegmentationPropagation( # registration_method=regflirt.FLIRT(use_verbose=args.verbose), # registration_method=niftyreg.RegAladin(use_verbose=False), dilation_radius=args.dilation_radius, dilation_kernel="Ball", ) data_preprocessing = dp.DataPreprocessing( stacks=stacks, segmentation_propagator=segmentation_propagator, use_cropping_to_mask=True, use_N4BiasFieldCorrector=args.bias_field_correction, target_stack_index=target_stack_index, boundary_i=args.boundary_stacks[0], boundary_j=args.boundary_stacks[1], boundary_k=args.boundary_stacks[2], unit="mm", ) data_preprocessing.run() time_data_preprocessing = data_preprocessing.get_computational_time() # Get preprocessed stacks stacks = data_preprocessing.get_preprocessed_stacks() # Define reference/target stack for registration and reconstruction if args.reference is not None: reference = st.Stack.from_filename( file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) else: reference = st.Stack.from_stack(stacks[target_stack_index]) # ------------------------Volume-to-Volume Registration-------------------- if len(stacks) > 1: if args.v2v_method == "FLIRT": # Define search angle ranges for FLIRT in all three dimensions search_angles = ["-searchr%s -%d %d" % (x, args.search_angle, args.search_angle) for x in ["x", "y", "z"]] options = (" ").join(search_angles) # options += " -noresample" vol_registration = regflirt.FLIRT( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, options=options, use_verbose=False, ) else: vol_registration = niftyreg.RegAladin( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, # options="-ln 2 -voff", use_verbose=False, ) v2vreg = pipeline.VolumeToVolumeRegistration( stacks=stacks, reference=reference, registration_method=vol_registration, verbose=debug, robust=args.v2v_robust, ) v2vreg.run() stacks = v2vreg.get_stacks() time_registration = v2vreg.get_computational_time() else: time_registration = ph.get_zero_time() # ---------------------------Intensity Correction-------------------------- if args.intensity_correction: ph.print_title("Intensity Correction") intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_reference_mask(True) intensity_corrector.use_stack_mask(True) intensity_corrector.use_verbose(False) for i, stack in enumerate(stacks): if i == target_stack_index: ph.print_info("Stack %d (%s): Reference image. Skipped." % ( i + 1, stack.get_filename())) continue else: ph.print_info("Stack %d (%s): Intensity Correction ... " % ( i + 1, stack.get_filename()), newline=False) intensity_corrector.set_stack(stack) intensity_corrector.set_reference( stacks[target_stack_index].get_resampled_stack( resampling_grid=stack.sitk, interpolator="NearestNeighbor", )) intensity_corrector.run_linear_intensity_correction() stacks[i] = intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # ---------------------------Create first volume--------------------------- time_tmp = ph.start_timing() # Isotropic resampling to define HR target space ph.print_title("Reconstruction Space Generation") HR_volume = reference.get_isotropically_resampled_stack( resolution=args.isotropic_resolution) ph.print_info( "Isotropic reconstruction space with %g mm resolution is created" % HR_volume.sitk.GetSpacing()[0]) if args.reference is None: # Create joint image mask in target space joint_image_mask_builder = imb.JointImageMaskBuilder( stacks=stacks, target=HR_volume, dilation_radius=1, ) joint_image_mask_builder.run() HR_volume = joint_image_mask_builder.get_stack() ph.print_info( "Isotropic reconstruction space is centered around " "joint stack masks. ") # Crop to space defined by mask (plus extra margin) HR_volume = HR_volume.get_cropped_stack_based_on_mask( boundary_i=args.extra_frame_target, boundary_j=args.extra_frame_target, boundary_k=args.extra_frame_target, unit="mm", ) # Create first volume # If outlier rejection is activated, eliminate obvious outliers early # from stack and re-run SDA to get initial volume without them ph.print_title("First Estimate of HR Volume") if args.outlier_rejection and threshold_v2v > -1: ph.print_subtitle("SDA Approximation") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma) SDA.run() HR_volume = SDA.get_reconstruction() # Identify and reject outliers ph.print_subtitle("Eliminate slice outliers (%s < %g)" % ( rejection_measure, threshold_v2v)) outlier_rejector = outre.OutlierRejector( stacks=stacks, reference=HR_volume, threshold=threshold_v2v, measure=rejection_measure, verbose=True, ) outlier_rejector.run() stacks = outlier_rejector.get_stacks() ph.print_subtitle("SDA Approximation Image") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma) SDA.run() HR_volume = SDA.get_reconstruction() ph.print_subtitle("SDA Approximation Image Mask") SDA = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma, sda_mask=True) SDA.run() # HR volume contains updated mask based on SDA HR_volume = SDA.get_reconstruction() HR_volume.set_filename(SDA.get_setting_specific_filename()) time_reconstruction = ph.stop_timing(time_tmp) if args.verbose: tmp = list(stacks) tmp.insert(0, HR_volume) sitkh.show_stacks(tmp, segmentation=HR_volume, viewer=args.viewer) # -----------Two-step Slice-to-Volume Registration-Reconstruction---------- if args.two_step_cycles > 0: # Slice-to-volume registration set-up if args.metric == "ANTSNeighborhoodCorrelation": metric_params = {"radius": args.metric_radius} else: metric_params = None registration = regsitk.SimpleItkRegistration( moving=HR_volume, use_fixed_mask=True, use_moving_mask=True, interpolator="Linear", metric=args.metric, metric_params=metric_params, use_multiresolution_framework=args.multiresolution, shrink_factors=args.shrink_factors, smoothing_sigmas=args.smoothing_sigmas, initializer_type="SelfGEOMETRY", optimizer="ConjugateGradientLineSearch", optimizer_params={ "learningRate": 1, "numberOfIterations": 100, "lineSearchUpperLimit": 2, }, scales_estimator="Jacobian", use_verbose=debug, ) # Volumetric reconstruction set-up if args.sda: recon_method = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.sigma, use_masks=args.use_masks_srr, ) alpha_range = [args.sigma, args.alpha] else: recon_method = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1", minimizer="lsmr", alpha=args.alpha_first, iter_max=np.min([args.iter_max_first, args.iter_max]), verbose=True, use_masks=args.use_masks_srr, ) alpha_range = [args.alpha_first, args.alpha] # Define the regularization parameters for the individual # reconstruction steps in the two-step cycles alphas = np.linspace( alpha_range[0], alpha_range[1], args.two_step_cycles) # Define outlier rejection threshold after each S2V-reg step thresholds = np.linspace( args.threshold_first, args.threshold, args.two_step_cycles) two_step_s2v_reg_recon = \ pipeline.TwoStepSliceToVolumeRegistrationReconstruction( stacks=stacks, reference=HR_volume, registration_method=registration, reconstruction_method=recon_method, cycles=args.two_step_cycles, alphas=alphas[0:args.two_step_cycles - 1], outlier_rejection=args.outlier_rejection, threshold_measure=rejection_measure, thresholds=thresholds, interleave=args.interleave, viewer=args.viewer, verbose=args.verbose, use_hierarchical_registration=args.s2v_hierarchical, ) two_step_s2v_reg_recon.run() HR_volume_iterations = \ two_step_s2v_reg_recon.get_iterative_reconstructions() time_registration += \ two_step_s2v_reg_recon.get_computational_time_registration() time_reconstruction += \ two_step_s2v_reg_recon.get_computational_time_reconstruction() stacks = two_step_s2v_reg_recon.get_stacks() # no two-step s2v-registration/reconstruction iterations else: HR_volume_iterations = [] # Write motion-correction results ph.print_title("Write Motion Correction Results") if args.write_motion_correction: dir_output_mc = os.path.join( dir_output, args.subfolder_motion_correction) ph.clear_directory(dir_output_mc) for stack in stacks: stack.write( dir_output_mc, write_stack=False, write_mask=False, write_slices=False, write_transforms=True, write_transforms_history=args.transforms_history, ) if args.outlier_rejection: deleted_slices_dic = {} for i, stack in enumerate(stacks): deleted_slices = stack.get_deleted_slice_numbers() deleted_slices_dic[stack.get_filename()] = deleted_slices # check whether any stack was removed entirely stacks0 = data_preprocessing.get_preprocessed_stacks() if len(stacks) != len(stacks0): stacks_remain = [s.get_filename() for s in stacks] for stack in stacks0: if stack.get_filename() in stacks_remain: continue # add info that all slices of this stack were rejected deleted_slices = [ slice.get_slice_number() for slice in stack.get_slices() ] deleted_slices_dic[stack.get_filename()] = deleted_slices ph.print_info( "All slices of stack '%s' were rejected entirely. " "Information added." % stack.get_filename()) ph.write_dictionary_to_json( deleted_slices_dic, os.path.join( dir_output, args.subfolder_motion_correction, "rejected_slices.json" ) ) # ---------------------Final Volumetric Reconstruction--------------------- ph.print_title("Final Volumetric Reconstruction") if args.sda: recon_method = sda.ScatteredDataApproximation( stacks, HR_volume, sigma=args.alpha, use_masks=args.use_masks_srr, ) else: if args.reconstruction_type in ["TVL2", "HuberL2"]: recon_method = pd.PrimalDualSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TV" if args.reconstruction_type == "TVL2" else "huber", iterations=args.iterations, use_masks=args.use_masks_srr, ) else: recon_method = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1" if args.reconstruction_type == "TK1L2" else "TK0", use_masks=args.use_masks_srr, ) recon_method.set_alpha(args.alpha) recon_method.set_iter_max(args.iter_max) recon_method.set_verbose(True) recon_method.run() time_reconstruction += recon_method.get_computational_time() HR_volume_final = recon_method.get_reconstruction() ph.print_subtitle("Final SDA Approximation Image Mask") SDA = sda.ScatteredDataApproximation( stacks, HR_volume_final, sigma=args.sigma, sda_mask=True) SDA.run() # HR volume contains updated mask based on SDA HR_volume_final = SDA.get_reconstruction() time_reconstruction += SDA.get_computational_time() elapsed_time_total = ph.stop_timing(time_start) # Write SRR result filename = recon_method.get_setting_specific_filename() HR_volume_final.set_filename(filename) dw.DataWriter.write_image( HR_volume_final.sitk, args.output, description=filename) dw.DataWriter.write_mask( HR_volume_final.sitk_mask, ph.append_to_filename(args.output, "_mask"), description=SDA.get_setting_specific_filename()) HR_volume_iterations.insert(0, HR_volume_final) for stack in stacks: HR_volume_iterations.append(stack) if args.verbose: sitkh.show_stacks( HR_volume_iterations, segmentation=HR_volume_final, viewer=args.viewer, ) # Summary ph.print_title("Summary") exe_file_info = os.path.basename(os.path.abspath(__file__)).split(".")[0] print("%s | Computational Time for Data Preprocessing: %s" % (exe_file_info, time_data_preprocessing)) print("%s | Computational Time for Registrations: %s" % (exe_file_info, time_registration)) print("%s | Computational Time for Reconstructions: %s" % (exe_file_info, time_reconstruction)) print("%s | Computational Time for Entire Reconstruction Pipeline: %s" % (exe_file_info, elapsed_time_total)) ph.print_line_separator() return 0
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Volumetric MRI reconstruction framework to reconstruct " "an isotropic, high-resolution 3D volume from multiple stacks of 2D " "slices with motion correction. The resolution of the computed " "Super-Resolution Reconstruction (SRR) is given by the in-plane " "spacing of the selected target stack. A region of interest can be " "specified by providing a mask for the selected target stack. Only " "this region will then be reconstructed by the SRR algorithm which " "can substantially reduce the computational time.", ) input_parser.add_dir_input() input_parser.add_filenames() input_parser.add_dir_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack_index(default=0) input_parser.add_search_angle(default=90) input_parser.add_multiresolution(default=0) input_parser.add_shrink_factors(default=[2, 1]) input_parser.add_smoothing_sigmas(default=[1, 0]) input_parser.add_sigma(default=0.9) input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_iterations(default=15) input_parser.add_alpha(default=0.02) input_parser.add_alpha_first(default=0.05) input_parser.add_iter_max(default=10) input_parser.add_iter_max_first(default=5) input_parser.add_dilation_radius(default=3) input_parser.add_extra_frame_target(default=10) input_parser.add_bias_field_correction(default=0) input_parser.add_intensity_correction(default=0) input_parser.add_isotropic_resolution(default=None) input_parser.add_log_script_execution(default=1) input_parser.add_subfolder_motion_correction() input_parser.add_provide_comparison(default=0) input_parser.add_subfolder_comparison() input_parser.add_write_motion_correction(default=1) input_parser.add_verbose(default=0) input_parser.add_two_step_cycles(default=3) input_parser.add_use_masks_srr(default=1) input_parser.add_boundary_stacks(default=[10, 10, 0]) input_parser.add_reference() input_parser.add_reference_mask() args = input_parser.parse_args() input_parser.print_arguments(args) # Write script execution call if args.log_script_execution: input_parser.write_performed_script_execution( os.path.abspath(__file__)) # Use FLIRT for volume-to-volume reg. step. Otherwise, RegAladin is used. use_flirt_for_v2v_registration = True # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") # Neither '--dir-input' nor '--filenames' was specified if args.filenames is not None and args.dir_input is not None: raise IOError("Provide input by either '--dir-input' or '--filenames' " "but not both together") # '--dir-input' specified elif args.dir_input is not None: data_reader = dr.ImageDirectoryReader(args.dir_input, suffix_mask=args.suffix_mask) # '--filenames' specified elif args.filenames is not None: data_reader = dr.MultipleImagesReader(args.filenames, suffix_mask=args.suffix_mask) else: raise IOError("Provide input by either '--dir-input' or '--filenames'") if len(args.boundary_stacks) is not 3: raise IOError( "Provide exactly three values for '--boundary-stacks' to define " "cropping in i-, j-, and k-dimension of the input stacks") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if all(s.is_unity_mask() is True for s in stacks): ph.print_warning("No mask is provided! " "Generated reconstruction space may be very big!") # ---------------------------Data Preprocessing--------------------------- ph.print_title("Data Preprocessing") segmentation_propagator = segprop.SegmentationPropagation( # registration_method=regflirt.FLIRT(use_verbose=args.verbose), dilation_radius=args.dilation_radius, dilation_kernel="Ball", ) data_preprocessing = dp.DataPreprocessing( stacks=stacks, segmentation_propagator=segmentation_propagator, use_cropping_to_mask=True, use_N4BiasFieldCorrector=args.bias_field_correction, use_intensity_correction=args.intensity_correction, target_stack_index=args.target_stack_index, boundary_i=args.boundary_stacks[0], boundary_j=args.boundary_stacks[1], boundary_k=args.boundary_stacks[2], unit="mm", ) data_preprocessing.run() time_data_preprocessing = data_preprocessing.get_computational_time() # Get preprocessed stacks stacks = data_preprocessing.get_preprocessed_stacks() # Define reference/target stack for registration and reconstruction if args.reference is not None: reference = st.Stack.from_filename(file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) else: reference = st.Stack.from_stack(stacks[args.target_stack_index]) # ------------------------Volume-to-Volume Registration-------------------- if args.two_step_cycles > 0: # Define search angle ranges for FLIRT in all three dimensions search_angles = [ "-searchr%s -%d %d" % (x, args.search_angle, args.search_angle) for x in ["x", "y", "z"] ] search_angles = (" ").join(search_angles) if use_flirt_for_v2v_registration: vol_registration = regflirt.FLIRT( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, options=search_angles, use_verbose=False, ) else: vol_registration = niftyreg.RegAladin( registration_type="Rigid", use_fixed_mask=True, use_moving_mask=True, use_verbose=False, ) v2vreg = pipeline.VolumeToVolumeRegistration( stacks=stacks, reference=reference, registration_method=vol_registration, verbose=args.verbose, ) v2vreg.run() stacks = v2vreg.get_stacks() time_registration = v2vreg.get_computational_time() else: time_registration = ph.get_zero_time() # ---------------------------Create first volume--------------------------- time_tmp = ph.start_timing() # Isotropic resampling to define HR target space ph.print_title("Reconstruction Space Generation") HR_volume = reference.get_isotropically_resampled_stack( resolution=args.isotropic_resolution) ph.print_info( "Isotropic reconstruction space with %g mm resolution is created" % HR_volume.sitk.GetSpacing()[0]) if args.reference is None: # Create joint image mask in target space joint_image_mask_builder = imb.JointImageMaskBuilder( stacks=stacks, target=HR_volume, dilation_radius=1, ) joint_image_mask_builder.run() HR_volume = joint_image_mask_builder.get_stack() ph.print_info("Isotropic reconstruction space is centered around " "joint stack masks. ") # Crop to space defined by mask (plus extra margin) HR_volume = HR_volume.get_cropped_stack_based_on_mask( boundary_i=args.extra_frame_target, boundary_j=args.extra_frame_target, boundary_k=args.extra_frame_target, unit="mm", ) # Scattered Data Approximation to get first estimate of HR volume ph.print_title("First Estimate of HR Volume") SDA = sda.ScatteredDataApproximation(stacks, HR_volume, sigma=args.sigma) SDA.run() HR_volume = SDA.get_reconstruction() time_reconstruction = ph.stop_timing(time_tmp) if args.verbose: tmp = list(stacks) tmp.insert(0, HR_volume) sitkh.show_stacks(tmp, segmentation=HR_volume) # ----------------Two-step Slice-to-Volume Registration SRR---------------- SRR = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1", minimizer="lsmr", alpha=args.alpha_first, iter_max=args.iter_max_first, verbose=True, use_masks=args.use_masks_srr, ) if args.two_step_cycles > 0: registration = regsitk.SimpleItkRegistration( moving=HR_volume, use_fixed_mask=True, use_moving_mask=True, use_verbose=args.verbose, interpolator="Linear", metric="Correlation", use_multiresolution_framework=args.multiresolution, shrink_factors=args.shrink_factors, smoothing_sigmas=args.smoothing_sigmas, initializer_type="SelfGEOMETRY", optimizer="ConjugateGradientLineSearch", optimizer_params={ "learningRate": 1, "numberOfIterations": 100, "lineSearchUpperLimit": 2, }, scales_estimator="Jacobian", ) two_step_s2v_reg_recon = \ pipeline.TwoStepSliceToVolumeRegistrationReconstruction( stacks=stacks, reference=HR_volume, registration_method=registration, reconstruction_method=SRR, cycles=args.two_step_cycles, alpha_range=[args.alpha_first, args.alpha], verbose=args.verbose, ) two_step_s2v_reg_recon.run() HR_volume_iterations = \ two_step_s2v_reg_recon.get_iterative_reconstructions() time_registration += \ two_step_s2v_reg_recon.get_computational_time_registration() time_reconstruction += \ two_step_s2v_reg_recon.get_computational_time_reconstruction() else: HR_volume_iterations = [] # Write motion-correction results if args.write_motion_correction: for stack in stacks: stack.write( os.path.join(args.dir_output, args.subfolder_motion_correction), write_mask=True, write_slices=True, write_transforms=True, suffix_mask=args.suffix_mask, ) # ------------------Final Super-Resolution Reconstruction------------------ ph.print_title("Final Super-Resolution Reconstruction") if args.reconstruction_type in ["TVL2", "HuberL2"]: SRR = pd.PrimalDualSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TV" if args.reconstruction_type == "TVL2" else "huber", iterations=args.iterations, ) else: SRR = tk.TikhonovSolver( stacks=stacks, reconstruction=HR_volume, reg_type="TK1" if args.reconstruction_type == "TK1L2" else "TK0", use_masks=args.use_masks_srr, ) SRR.set_alpha(args.alpha) SRR.set_iter_max(args.iter_max) SRR.set_verbose(True) SRR.run() time_reconstruction += SRR.get_computational_time() elapsed_time_total = ph.stop_timing(time_start) # Write SRR result HR_volume_final = SRR.get_reconstruction() HR_volume_final.set_filename(SRR.get_setting_specific_filename()) HR_volume_final.write(args.dir_output, write_mask=True, suffix_mask=args.suffix_mask) HR_volume_iterations.insert(0, HR_volume_final) for stack in stacks: HR_volume_iterations.append(stack) if args.verbose and not args.provide_comparison: sitkh.show_stacks(HR_volume_iterations, segmentation=HR_volume) # HR_volume_final.show() # Show SRR together with linearly resampled input data. # Additionally, a script is generated to open files if args.provide_comparison: sitkh.show_stacks( HR_volume_iterations, segmentation=HR_volume, show_comparison_file=args.provide_comparison, dir_output=os.path.join(args.dir_output, args.subfolder_comparison), ) # Summary ph.print_title("Summary") print("Computational Time for Data Preprocessing: %s" % (time_data_preprocessing)) print("Computational Time for Registrations: %s" % (time_registration)) print("Computational Time for Reconstructions: %s" % (time_reconstruction)) print("Computational Time for Entire Reconstruction Pipeline: %s" % (elapsed_time_total)) ph.print_line_separator() return 0
def main(): # Set print options np.set_printoptions(precision=3) pd.set_option('display.width', 1000) input_parser = InputArgparser(description=".", ) input_parser.add_filenames(required=True) input_parser.add_reference(required=True) input_parser.add_reference_mask() input_parser.add_dir_output(required=False) input_parser.add_measures( default=["PSNR", "RMSE", "MAE", "SSIM", "NCC", "NMI"]) input_parser.add_verbose(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) ph.print_title("Image similarity") data_reader = dr.MultipleImagesReader(args.filenames) data_reader.read_data() stacks = data_reader.get_data() reference = st.Stack.from_filename(args.reference, args.reference_mask) for stack in stacks: try: stack.sitk - reference.sitk except RuntimeError as e: raise IOError( "All provided images must be at the same image space") x_ref = sitk.GetArrayFromImage(reference.sitk) if args.reference_mask is None: indices = np.where(x_ref != np.inf) else: x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask) indices = np.where(x_ref_mask > 0) measures_dic = { m: lambda x, m=m: SimilarityMeasures.similarity_measures[m] (x[indices], x_ref[indices]) # SimilarityMeasures.similarity_measures[m](x, x_ref) for m in args.measures } observer = obs.Observer() observer.set_measures(measures_dic) for stack in stacks: nda = sitk.GetArrayFromImage(stack.sitk) observer.add_x(nda) if args.verbose: stacks_comparison = [s for s in stacks] stacks_comparison.insert(0, reference) sitkh.show_stacks( stacks_comparison, segmentation=reference, ) observer.compute_measures() measures = observer.get_measures() # Store information in array error = np.zeros((len(stacks), len(measures))) cols = measures rows = [] for i_stack, stack in enumerate(stacks): error[i_stack, :] = np.array([measures[m][i_stack] for m in measures]) rows.append(stack.get_filename()) header = "# Ref: %s, Ref-Mask: %d, %s \n" % ( reference.get_filename(), args.reference_mask is None, ph.get_time_stamp(), ) header += "# %s\n" % ("\t").join(measures) path_to_file_filenames = os.path.join(args.dir_output, "filenames.txt") path_to_file_similarities = os.path.join(args.dir_output, "similarities.txt") # Write to files ph.write_to_file(path_to_file_similarities, header) ph.write_array_to_file(path_to_file_similarities, error, verbose=False) text = header text += "%s\n" % "\n".join(rows) ph.write_to_file(path_to_file_filenames, text) # Print to screen ph.print_subtitle("Computed Similarities") df = pd.DataFrame(error, rows, cols) print(df) return 0
def main(): time_start = ph.start_timing() # Set print options for numpy np.set_printoptions(precision=3) # Read input input_parser = InputArgparser( description="Script to study reconstruction parameters and their " "impact on the volumetric reconstruction quality.", ) input_parser.add_dir_input() input_parser.add_filenames() input_parser.add_image_selection() input_parser.add_dir_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_reconstruction_space() input_parser.add_reference( help="Path to reference NIfTI image file. If given the volumetric " "reconstructed is performed in this physical space. " "Either a reconstruction space or a reference must be provided", required=False) input_parser.add_reference_mask(default=None) input_parser.add_study_name() input_parser.add_reconstruction_type(default="TK1L2") input_parser.add_measures(default=["PSNR", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_tv_solver(default="PD") input_parser.add_iterations(default=50) input_parser.add_rho(default=0.1) input_parser.add_iter_max(default=10) input_parser.add_minimizer(default="lsmr") input_parser.add_alpha(default=0.01) input_parser.add_data_loss(default="linear") input_parser.add_data_loss_scale(default=1) input_parser.add_log_script_execution(default=1) input_parser.add_verbose(default=1) # Range for parameter sweeps input_parser.add_alpha_range(default=[0.001, 0.05, 20]) # TK1L2 # input_parser.add_alpha_range(default=[0.001, 0.003, 10]) # TVL2, HuberL2 input_parser.add_data_losses( # default=["linear", "arctan"] ) input_parser.add_data_loss_scale_range( # default=[0.1, 1.5, 2] ) args = input_parser.parse_args() input_parser.print_arguments(args) if args.reference is None and args.reconstruction_space is None: raise IOError("Either reference (--reference) or reconstruction space " "(--reconstruction-space) must be provided.") # Write script execution call if args.log_script_execution: input_parser.write_performed_script_execution( os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") # Neither '--dir-input' nor '--filenames' was specified if args.filenames is not None and args.dir_input is not None: raise IOError( "Provide input by either '--dir-input' or '--filenames' " "but not both together") # '--dir-input' specified elif args.dir_input is not None: data_reader = dr.ImageSlicesDirectoryReader( path_to_directory=args.dir_input, suffix_mask=args.suffix_mask, image_selection=args.image_selection) # '--filenames' specified elif args.filenames is not None: data_reader = dr.MultipleImagesReader( args.filenames, suffix_mask=args.suffix_mask) else: raise IOError( "Provide input by either '--dir-input' or '--filenames'") data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) if args.reference is not None: reference = st.Stack.from_filename( file_path=args.reference, file_path_mask=args.reference_mask, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack(reference.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = sitk.GetArrayFromImage(reference.sitk).flatten() x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten() else: reconstruction_space = st.Stack.from_filename( file_path=args.reconstruction_space, extract_slices=False) reconstruction_space = stacks[0].get_resampled_stack( reconstruction_space.sitk) reconstruction_space = \ reconstruction_space.get_stack_multiplied_with_mask() x_ref = None x_ref_mask = None # ----------------------------Set Up Parameters---------------------------- parameters = {} parameters["alpha"] = np.linspace( args.alpha_range[0], args.alpha_range[1], int(args.alpha_range[2])) if args.data_losses is not None: parameters["data_loss"] = args.data_losses if args.data_loss_scale_range is not None: parameters["data_loss_scale"] = np.linspace( args.data_loss_scale_range[0], args.data_loss_scale_range[1], int(args.data_loss_scale_range[2])) # --------------------------Set Up Parameter Study------------------------- if args.study_name is None: name = args.reconstruction_type else: name = args.study_name reconstruction_info = { "shape": reconstruction_space.sitk.GetSize()[::-1], "origin": reconstruction_space.sitk.GetOrigin(), "spacing": reconstruction_space.sitk.GetSpacing(), "direction": reconstruction_space.sitk.GetDirection(), } # Create Tikhonov solver from which all information can be extracted # (also for other reconstruction types) tmp = tk.TikhonovSolver( stacks=stacks, reconstruction=reconstruction_space, alpha=args.alpha, iter_max=args.iter_max, data_loss=args.data_loss, data_loss_scale=args.data_loss_scale, reg_type="TK1", minimizer=args.minimizer, verbose=args.verbose, ) solver = tmp.get_solver() parameter_study_interface = \ deconv_interface.DeconvolutionParameterStudyInterface( A=solver.get_A(), A_adj=solver.get_A_adj(), D=solver.get_B(), D_adj=solver.get_B_adj(), b=solver.get_b(), x0=solver.get_x0(), alpha=solver.get_alpha(), x_scale=solver.get_x_scale(), data_loss=solver.get_data_loss(), data_loss_scale=solver.get_data_loss_scale(), iter_max=solver.get_iter_max(), minimizer=solver.get_minimizer(), iterations=args.iterations, measures=args.measures, dimension=3, L2=16./reconstruction_space.sitk.GetSpacing()[0]**2, reconstruction_type=args.reconstruction_type, rho=args.rho, dir_output=args.dir_output, parameters=parameters, name=name, reconstruction_info=reconstruction_info, x_ref=x_ref, x_ref_mask=x_ref_mask, tv_solver=args.tv_solver, verbose=args.verbose, ) parameter_study_interface.set_up_parameter_study() parameter_study = parameter_study_interface.get_parameter_study() # Run parameter study parameter_study.run() print("\nComputational time for Deconvolution Parameter Study %s: %s" % (name, parameter_study.get_computational_time())) return 0
def main(): time_start = ph.start_timing() np.set_printoptions(precision=3) input_parser = InputArgparser( description="Run reconstruction pipeline including " "(i) bias field correction, " "(ii) volumetric reconstruction in subject space, " "and (iii) volumetric reconstruction in template space.", ) input_parser.add_filenames(required=True) input_parser.add_filenames_masks(required=True) input_parser.add_target_stack(required=False) input_parser.add_suffix_mask(default="''") input_parser.add_dir_output(required=True) input_parser.add_alpha(default=0.01) input_parser.add_verbose(default=0) input_parser.add_gestational_age(required=False) input_parser.add_prefix_output(default="") input_parser.add_search_angle(default=180) input_parser.add_multiresolution(default=0) input_parser.add_log_config(default=1) input_parser.add_isotropic_resolution() input_parser.add_reference() input_parser.add_reference_mask() input_parser.add_bias_field_correction(default=1) input_parser.add_intensity_correction(default=1) input_parser.add_iter_max(default=10) input_parser.add_two_step_cycles(default=3) input_parser.add_option( option_string="--run-bias-field-correction", type=int, help="Turn on/off bias field correction. " "If off, it is assumed that this step was already performed", default=1) input_parser.add_option( option_string="--run-recon-subject-space", type=int, help="Turn on/off reconstruction in subject space. " "If off, it is assumed that this step was already performed", default=1) input_parser.add_option( option_string="--run-recon-template-space", type=int, help="Turn on/off reconstruction in template space. " "If off, it is assumed that this step was already performed", default=1) input_parser.add_option( option_string="--run-data-vs-simulated-data", type=int, help="Turn on/off comparison of data vs data simulated from the " "obtained volumetric reconstruction. " "If off, it is assumed that this step was already performed", default=0) input_parser.add_option( option_string="--initial-transform", type=str, help="Set initial transform to be used for register_image.", default=None) input_parser.add_outlier_rejection(default=1) input_parser.add_argument( "--sda", "-sda", action='store_true', help="If given, the volume is reconstructed using " "Scattered Data Approximation (Vercauteren et al., 2006). " "--alpha is considered the value for the standard deviation then. " "Recommended value is, e.g., --alpha 0.8") args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) filename_srr = "srr" dir_output_preprocessing = os.path.join(args.dir_output, "preprocessing_n4itk") dir_output_recon_subject_space = os.path.join(args.dir_output, "recon_subject_space") dir_output_recon_template_space = os.path.join(args.dir_output, "recon_template_space") dir_output_data_vs_simulatd_data = os.path.join(args.dir_output, "data_vs_simulated_data") srr_subject = os.path.join(dir_output_recon_subject_space, "%s_subject.nii.gz" % filename_srr) srr_subject_mask = ph.append_to_filename(srr_subject, "_mask") srr_template = os.path.join(dir_output_recon_template_space, "%s_template.nii.gz" % filename_srr) srr_template_mask = ph.append_to_filename(srr_template, "_mask") trafo_template = os.path.join(dir_output_recon_template_space, "registration_transform_sitk.txt") if args.target_stack is None: target_stack = args.filenames[0] else: target_stack = args.target_stack if args.bias_field_correction and args.run_bias_field_correction: for i, f in enumerate(args.filenames): output = os.path.join(dir_output_preprocessing, os.path.basename(f)) cmd_args = [] cmd_args.append("--filename %s" % f) cmd_args.append("--filename-mask %s" % args.filenames_masks[i]) cmd_args.append("--output %s" % output) # cmd_args.append("--verbose %d" % args.verbose) cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args) time_start_bias = ph.start_timing() exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Bias field correction failed") elapsed_time_bias = ph.stop_timing(time_start_bias) filenames = [ os.path.join(dir_output_preprocessing, os.path.basename(f)) for f in args.filenames ] elif args.bias_field_correction and not args.run_bias_field_correction: elapsed_time_bias = ph.get_zero_time() filenames = [ os.path.join(dir_output_preprocessing, os.path.basename(f)) for f in args.filenames ] else: elapsed_time_bias = ph.get_zero_time() filenames = args.filenames if args.run_recon_subject_space: target_stack_index = args.filenames.index(target_stack) cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames)) if args.filenames_masks is not None: cmd_args.append("--filenames-masks %s" % (" ").join(args.filenames_masks)) cmd_args.append("--multiresolution %d" % args.multiresolution) cmd_args.append("--target-stack-index %d" % target_stack_index) cmd_args.append("--output %s" % srr_subject) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--intensity-correction %d" % args.intensity_correction) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--two-step-cycles %d" % args.two_step_cycles) cmd_args.append("--outlier-rejection %d" % args.outlier_rejection) cmd_args.append("--verbose %d" % args.verbose) if args.isotropic_resolution is not None: cmd_args.append("--isotropic-resolution %f" % args.isotropic_resolution) if args.reference is not None: cmd_args.append("--reference %s" % args.reference) if args.reference_mask is not None: cmd_args.append("--reference-mask %s" % args.reference_mask) if args.sda: cmd_args.append("--sda") cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args) time_start_volrec = ph.start_timing() exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in subject space failed") # Compute SRR mask in subject space # (Approximated using SDA within reconstruct_volume) if 0: dir_motion_correction = os.path.join( dir_output_recon_subject_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % " ".join(args.filenames_masks)) cmd_args.append("--dir-input-mc %s" % dir_motion_correction) cmd_args.append("--output %s" % srr_subject_mask) cmd_args.append("--reconstruction-space %s" % srr_subject) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--mask") if args.sda: cmd_args.append("--sda") cmd_args.append("--alpha 1") else: cmd_args.append("--alpha 0.1") cmd_args.append("--iter-max 5") cmd = (" ").join(cmd_args) ph.execute_command(cmd) elapsed_time_volrec = ph.stop_timing(time_start_volrec) else: elapsed_time_volrec = ph.get_zero_time() if args.run_recon_template_space: if args.gestational_age is None: template_stack_estimator = \ tse.TemplateStackEstimator.from_mask(srr_subject_mask) gestational_age = template_stack_estimator.get_estimated_gw() ph.print_info("Estimated gestational age: %d" % gestational_age) else: gestational_age = args.gestational_age template = os.path.join(DIR_TEMPLATES, "STA%d.nii.gz" % gestational_age) template_mask = os.path.join(DIR_TEMPLATES, "STA%d_mask.nii.gz" % gestational_age) cmd_args = [] cmd_args.append("--fixed %s" % template) cmd_args.append("--moving %s" % srr_subject) cmd_args.append("--fixed-mask %s" % template_mask) cmd_args.append("--moving-mask %s" % srr_subject_mask) cmd_args.append( "--dir-input-mc %s" % os.path.join(dir_output_recon_subject_space, "motion_correction")) cmd_args.append("--output %s" % trafo_template) cmd_args.append("--verbose %s" % args.verbose) if args.initial_transform is not None: cmd_args.append("--initial-transform %s" % args.initial_transform) cmd_args.append("--use-flirt 0") cmd_args.append("--test-ap-flip 0") cmd = "niftymic_register_image %s" % (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Registration to template space failed") # reconstruct volume in template space # pattern = "[a-zA-Z0-9_.]+(ResamplingToTemplateSpace.nii.gz)" # p = re.compile(pattern) # reconstruction_space = [ # os.path.join(dir_output_recon_template_space, p.match(f).group(0)) # for f in os.listdir(dir_output_recon_template_space) # if p.match(f)][0] dir_input_mc = os.path.join(dir_output_recon_template_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % (" ").join(filenames)) cmd_args.append("--dir-input-mc %s" % dir_input_mc) cmd_args.append("--output %s" % srr_template) cmd_args.append("--reconstruction-space %s" % template) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--verbose %s" % args.verbose) if args.sda: cmd_args.append("--sda") cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in template space failed") # Compute SRR mask in template space if 1: dir_motion_correction = os.path.join( dir_output_recon_template_space, "motion_correction") cmd_args = ["niftymic_reconstruct_volume_from_slices"] cmd_args.append("--filenames %s" % " ".join(args.filenames_masks)) cmd_args.append("--dir-input-mc %s" % dir_motion_correction) cmd_args.append("--output %s" % srr_template_mask) cmd_args.append("--reconstruction-space %s" % srr_template) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--mask") if args.sda: cmd_args.append("--sda") cmd_args.append("--alpha 1") else: cmd_args.append("--alpha 0.1") cmd_args.append("--iter-max 5") cmd = (" ").join(cmd_args) ph.execute_command(cmd) # Copy SRR to output directory output = "%sSRR_Stacks%d.nii.gz" % (args.prefix_output, len(args.filenames)) path_to_output = os.path.join(args.dir_output, output) cmd = "cp -p %s %s" % (srr_template, path_to_output) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Copy of SRR to output directory failed") # Multiply template mask with reconstruction cmd_args = ["niftymic_multiply"] fnames = [ srr_template, srr_template_mask, ] output_masked = "Masked_%s" % output path_to_output_masked = os.path.join(args.dir_output, output_masked) cmd_args.append("--filenames %s" % " ".join(fnames)) cmd_args.append("--output %s" % path_to_output_masked) cmd = (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR brain masking failed") else: elapsed_time_template = ph.get_zero_time() if args.run_data_vs_simulated_data: dir_input_mc = os.path.join(dir_output_recon_template_space, "motion_correction") # Get simulated/projected slices cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames)) if args.filenames_masks is not None: cmd_args.append("--filenames-masks %s" % (" ").join(args.filenames_masks)) cmd_args.append("--dir-input-mc %s" % dir_input_mc) cmd_args.append("--dir-output %s" % dir_output_data_vs_simulatd_data) cmd_args.append("--reconstruction %s" % srr_template) cmd_args.append("--copy-data 1") cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) # cmd_args.append("--verbose %s" % args.verbose) exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR slice projections failed") filenames_simulated = [ os.path.join(dir_output_data_vs_simulatd_data, os.path.basename(f)) for f in filenames ] dir_output_evaluation = os.path.join(dir_output_data_vs_simulatd_data, "evaluation") dir_output_figures = os.path.join(dir_output_data_vs_simulatd_data, "figures") dir_output_side_by_side = os.path.join(dir_output_figures, "side-by-side") # Evaluate slice similarities to ground truth cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) if args.filenames_masks is not None: cmd_args.append("--filenames-masks %s" % (" ").join(args.filenames_masks)) cmd_args.append("--suffix-mask '%s'" % args.suffix_mask) cmd_args.append("--measures NCC SSIM") cmd_args.append("--dir-output %s" % dir_output_evaluation) exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Evaluation of slice similarities failed") # Generate figures showing the quantitative comparison cmd_args = [] cmd_args.append("--dir-input %s" % dir_output_evaluation) cmd_args.append("--dir-output %s" % dir_output_figures) exe = os.path.abspath( show_evaluated_simulated_stack_similarity.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: ph.print_warning("Visualization of slice similarities failed") # Generate pdfs showing all the side-by-side comparisons cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) cmd_args.append("--dir-output %s" % dir_output_side_by_side) exe = os.path.abspath( export_side_by_side_simulated_vs_original_slice_comparison.__file__ ) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Generation of PDF overview failed") ph.print_title("Summary") print("Computational Time for Bias Field Correction: %s" % elapsed_time_bias) print("Computational Time for Volumetric Reconstruction: %s" % elapsed_time_volrec) print("Computational Time for Pipeline: %s" % ph.stop_timing(time_start)) return 0
def main(): time_start = ph.start_timing() # Set print options np.set_printoptions(precision=3) pd.set_option('display.width', 1000) input_parser = InputArgparser(description=".", ) input_parser.add_filenames() input_parser.add_filenames_masks() input_parser.add_dir_input_mc() input_parser.add_suffix_mask(default="_mask") input_parser.add_reference(required=True) input_parser.add_reference_mask() input_parser.add_dir_output(required=False) input_parser.add_log_config(default=1) input_parser.add_measures(default=["PSNR", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_verbose(default=0) input_parser.add_slice_thicknesses(default=None) input_parser.add_option(option_string="--use-reference-mask", type=int, default=1) input_parser.add_option(option_string="--use-slice-masks", type=int, default=1) args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) reference = st.Stack.from_filename(args.reference, args.reference_mask) ph.print_title("Slice Residual Similarity") residual_evaluator = res_ev.ResidualEvaluator( stacks=stacks, reference=reference, measures=args.measures, use_reference_mask=args.use_reference_mask, use_slice_masks=args.use_slice_masks, ) residual_evaluator.compute_slice_projections() residual_evaluator.evaluate_slice_similarities() residual_evaluator.write_slice_similarities(args.dir_output) elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Slice Residual Evaluation: %s" % (elapsed_time)) return 0
def main(): time_start = ph.start_timing() # Set print options np.set_printoptions(precision=3) pd.set_option('display.width', 1000) input_parser = InputArgparser( description=".", ) input_parser.add_filenames() input_parser.add_filenames_masks() input_parser.add_dir_input_mc() input_parser.add_suffix_mask(default="_mask") input_parser.add_reference(required=True) input_parser.add_reference_mask() input_parser.add_dir_output(required=False) input_parser.add_log_config(default=1) input_parser.add_measures( default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"]) input_parser.add_verbose(default=0) input_parser.add_target_stack(default=None) input_parser.add_intensity_correction(default=1) input_parser.add_slice_thicknesses(default=None) input_parser.add_option( option_string="--use-reference-mask", type=int, default=1) input_parser.add_option( option_string="--use-slice-masks", type=int, default=1) args = input_parser.parse_args() input_parser.print_arguments(args) if args.log_config: input_parser.log_config(os.path.abspath(__file__)) # --------------------------------Read Data-------------------------------- ph.print_title("Read Data") data_reader = dr.MultipleImagesReader( file_paths=args.filenames, file_paths_masks=args.filenames_masks, suffix_mask=args.suffix_mask, dir_motion_correction=args.dir_input_mc, stacks_slice_thicknesses=args.slice_thicknesses, ) data_reader.read_data() stacks = data_reader.get_data() ph.print_info("%d input stacks read for further processing" % len(stacks)) # Specify target stack for intensity correction and reconstruction space if args.target_stack is None: target_stack_index = 0 else: filenames = ["%s.nii.gz" % s.get_filename() for s in stacks] filename_target_stack = os.path.basename(args.target_stack) try: target_stack_index = filenames.index(filename_target_stack) except ValueError as e: raise ValueError( "--target-stack must correspond to an image as provided by " "--filenames") # ---------------------------Intensity Correction-------------------------- if args.intensity_correction: ph.print_title("Intensity Correction") intensity_corrector = ic.IntensityCorrection() intensity_corrector.use_individual_slice_correction(False) intensity_corrector.use_stack_mask(True) intensity_corrector.use_reference_mask(True) intensity_corrector.use_verbose(False) for i, stack in enumerate(stacks): if i == target_stack_index: ph.print_info("Stack %d (%s): Reference image. Skipped." % ( i + 1, stack.get_filename())) continue else: ph.print_info("Stack %d (%s): Intensity Correction ... " % ( i + 1, stack.get_filename()), newline=False) intensity_corrector.set_stack(stack) intensity_corrector.set_reference( stacks[target_stack_index].get_resampled_stack( resampling_grid=stack.sitk, interpolator="NearestNeighbor", )) intensity_corrector.run_linear_intensity_correction() stacks[i] = intensity_corrector.get_intensity_corrected_stack() print("done (c1 = %g) " % intensity_corrector.get_intensity_correction_coefficients()) # ----------------------- Slice Residual Similarity ----------------------- reference = st.Stack.from_filename(args.reference, args.reference_mask) ph.print_title("Slice Residual Similarity") residual_evaluator = res_ev.ResidualEvaluator( stacks=stacks, reference=reference, measures=args.measures, use_reference_mask=args.use_reference_mask, use_slice_masks=args.use_slice_masks, ) residual_evaluator.compute_slice_projections() residual_evaluator.evaluate_slice_similarities() residual_evaluator.write_slice_similarities(args.dir_output) elapsed_time = ph.stop_timing(time_start) ph.print_title("Summary") print("Computational Time for Slice Residual Evaluation: %s" % (elapsed_time)) return 0
def main(): time_start = ph.start_timing() np.set_printoptions(precision=3) input_parser = InputArgparser( description="Run reconstruction pipeline including " "(i) preprocessing (bias field correction + intensity correction), " "(ii) volumetric reconstruction in subject space, " "and (iii) volumetric reconstruction in template space.", ) input_parser.add_dir_input(required=True) input_parser.add_dir_mask(required=True) input_parser.add_dir_output(required=True) input_parser.add_suffix_mask(default="_mask") input_parser.add_target_stack(required=False) input_parser.add_alpha(default=0.01) input_parser.add_verbose(default=0) input_parser.add_gestational_age(required=False) input_parser.add_prefix_output(default="") input_parser.add_search_angle(default=180) input_parser.add_multiresolution(default=0) input_parser.add_log_script_execution(default=1) input_parser.add_dir_input_templates(default=DIR_TEMPLATES) input_parser.add_isotropic_resolution() input_parser.add_reference() input_parser.add_reference_mask() input_parser.add_bias_field_correction(default=1) input_parser.add_intensity_correction(default=1) input_parser.add_iter_max(default=10) input_parser.add_two_step_cycles(default=3) input_parser.add_option( option_string="--run-recon-subject-space", type=int, help="Turn on/off reconstruction in subject space", default=1) input_parser.add_option( option_string="--run-recon-template-space", type=int, help="Turn on/off reconstruction in template space", default=1) input_parser.add_option( option_string="--run-data-vs-simulated-data", type=int, help="Turn on/off comparison of data vs data simulated from the " "obtained volumetric reconstruction", default=1) input_parser.add_outlier_rejection(default=0) input_parser.add_use_robust_registration(default=0) args = input_parser.parse_args() input_parser.print_arguments(args) # Write script execution call if args.log_script_execution: input_parser.write_performed_script_execution( os.path.abspath(__file__)) dir_output_recon_subject_space = os.path.join( args.dir_output, "recon_subject_space") dir_output_recon_template_space = os.path.join( args.dir_output, "recon_template_space") dir_output_data_vs_simulatd_data = os.path.join( args.dir_output, "data_vs_simulated_data") # if args.run_recon_template_space and args.gestational_age is None: # raise IOError("Gestational age must be set in order to pick the " # "right template") # get input stack names files = os.listdir(args.dir_input) input_files = [] mask_files = [] for file in files: if (".nii" in file): input_files.append("{0:}/{1:}".format(args.dir_input, file)) file_prefix = file[:-7] if (".nii.gz" in file) else file[:-4] mask_name = "{0:}/{1:}.nii.gz".format(args.dir_mask, file_prefix) if(not os.path.isfile(mask_name)): mask_name = "{0:}/{1:}.nii".format(args.dir_mask, file_prefix) assert(os.path.isfile(mask_name)) mask_files.append(mask_name) if args.target_stack is None: target_stack = input_files[0] else: target_stack = input_files if args.run_recon_subject_space: target_stack_index = input_files.index(target_stack) cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(input_files)) cmd_args.append("--filenames-masks %s" % (" ").join(mask_files)) cmd_args.append("--multiresolution %d" % args.multiresolution) cmd_args.append("--target-stack-index %d" % target_stack_index) cmd_args.append("--dir-output %s" % dir_output_recon_subject_space) # cmd_args.append("--suffix-mask %s" % args.suffix_mask) cmd_args.append("--intensity-correction %d" % args.intensity_correction) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--two-step-cycles %d" % args.two_step_cycles) cmd_args.append("--outlier-rejection %d" % args.outlier_rejection) cmd_args.append("--use-robust-registration %d" % args.use_robust_registration) cmd_args.append("--verbose %d" % args.verbose) if args.isotropic_resolution is not None: cmd_args.append("--isotropic-resolution %f" % args.isotropic_resolution) if args.reference is not None: cmd_args.append("--reference %s" % args.reference) if args.reference_mask is not None: cmd_args.append("--reference-mask %s" % args.reference_mask) cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args) time_start_volrec = ph.start_timing() exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in subject space failed") elapsed_time_volrec = ph.stop_timing(time_start_volrec) else: elapsed_time_volrec = ph.get_zero_time() if args.run_recon_template_space: # register recon to template space pattern = "[a-zA-Z0-9_]+(stacks)[a-zA-Z0-9_]+(.nii.gz)" p = re.compile(pattern) reconstruction = [ os.path.join( dir_output_recon_subject_space, p.match(f).group(0)) for f in os.listdir(dir_output_recon_subject_space) if p.match(f)][0] if('mask_manual' in args.dir_output): # find the corresponding template by volume matching reconstruction_mask = reconstruction if(not ("_mask" in reconstruction)): reconstruction_mask = ph.append_to_filename(reconstruction, "_mask") template_stack_estimator = \ tse.TemplateStackEstimator.from_mask( reconstruction_mask, args.dir_input_templates) template_mask = template_stack_estimator.get_path_to_template() template = template_mask.replace('_mask_dil.nii.gz', '.nii.gz') print('template name', template) # template = os.path.join( # args.dir_input_templates, # "STA%d.nii.gz" % args.gestational_age) # template_mask = os.path.join( # args.dir_input_templates, # "STA%d_mask.nii.gz" % args.gestational_age) else: template_folder = args.dir_output + "/../../mask_manual/reconstruct_outlier_gpr/" file_names = os.listdir(template_folder) template_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" not in item)] mask_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" in item)] template = os.path.join(template_folder, template_names[0]) template_mask = os.path.join(template_folder, mask_names[0]) cmd_args = [] cmd_args.append("--moving %s" % reconstruction) cmd_args.append("--fixed %s" % template) # if(use_spatiotemporal_template is False): # cmd_args.append("--use-fixed-mask 1") # added by Guotai # cmd_args.append("--template-mask %s" % template_mask) # micheal's code cmd_args.append("--dir-input %s" % os.path.join( dir_output_recon_subject_space, "motion_correction")) cmd_args.append("--dir-output %s" % dir_output_recon_template_space) cmd_args.append("--suffix-mask %s" % args.suffix_mask) cmd_args.append("--verbose %s" % args.verbose) cmd = "niftymic_register_image %s" % (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Registration to template space failed") # reconstruct volume in template space # pattern = "[a-zA-Z0-9_.]+(ResamplingToTemplateSpace.nii.gz)" # p = re.compile(pattern) # reconstruction_space = [ # os.path.join(dir_output_recon_template_space, p.match(f).group(0)) # for f in os.listdir(dir_output_recon_template_space) # if p.match(f)][0] dir_input = os.path.join( dir_output_recon_template_space, "motion_correction") cmd_args = [] cmd_args.append("--dir-input %s" % dir_input) cmd_args.append("--dir-output %s" % dir_output_recon_template_space) cmd_args.append("--reconstruction-space %s" % template) cmd_args.append("--iter-max %d" % args.iter_max) cmd_args.append("--alpha %s" % args.alpha) cmd_args.append("--suffix-mask %s" % args.suffix_mask) cmd = "niftymic_reconstruct_volume_from_slices %s" % \ (" ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Reconstruction in template space failed") pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)" p = re.compile(pattern) reconstruction = { p.match(f).group(1): os.path.join( dir_output_recon_template_space, p.match(f).group(0)) for f in os.listdir(dir_output_recon_template_space) if p.match(f) and not p.match(f).group(0).endswith( "ResamplingToTemplateSpace.nii.gz")} key = reconstruction.keys()[0] path_to_recon = reconstruction[key] # Copy SRR to output directory output = "%sSRR_%s_GW%d.nii.gz" % ( args.prefix_output, key, args.gestational_age) path_to_output = os.path.join(args.dir_output, output) cmd = "cp -p %s %s" % (path_to_recon, path_to_output) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Copy of SRR to output directory failed") # Multiply template mask with reconstruction cmd_args = [] cmd_args.append("--filename %s" % path_to_output) cmd_args.append("--gestational-age %s" % args.gestational_age) cmd_args.append("--verbose %s" % args.verbose) cmd_args.append("--dir-input-templates %s " % args.dir_input_templates) cmd = "niftymic_multiply_stack_with_mask %s" % ( " ").join(cmd_args) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR brain masking failed") else: elapsed_time_template = ph.get_zero_time() if args.run_data_vs_simulated_data: dir_input = os.path.join( dir_output_recon_template_space, "motion_correction") pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)" # pattern = "Masked_[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)" p = re.compile(pattern) reconstruction = { p.match(f).group(1): os.path.join( dir_output_recon_template_space, p.match(f).group(0)) for f in os.listdir(dir_output_recon_template_space) if p.match(f) and not p.match(f).group(0).endswith( "ResamplingToTemplateSpace.nii.gz")} key = reconstruction.keys()[0] path_to_recon = reconstruction[key] # Get simulated/projected slices cmd_args = [] cmd_args.append("--dir-input %s" % dir_input) cmd_args.append("--dir-output %s" % dir_output_data_vs_simulatd_data) cmd_args.append("--reconstruction %s" % path_to_recon) cmd_args.append("--copy-data 1") cmd_args.append("--suffix-mask %s" % args.suffix_mask) # cmd_args.append("--verbose %s" % args.verbose) exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("SRR slice projections failed") filenames_simulated = [ os.path.join(dir_output_data_vs_simulatd_data, os.path.basename(f)) for f in input_files] dir_output_evaluation = os.path.join( dir_output_data_vs_simulatd_data, "evaluation") dir_output_figures = os.path.join( dir_output_data_vs_simulatd_data, "figures") dir_output_side_by_side = os.path.join( dir_output_figures, "side-by-side") # Evaluate slice similarities to ground truth cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) cmd_args.append("--suffix-mask %s" % args.suffix_mask) cmd_args.append("--measures NCC SSIM") cmd_args.append("--dir-output %s" % dir_output_evaluation) exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Evaluation of slice similarities failed") # Generate figures showing the quantitative comparison cmd_args = [] cmd_args.append("--dir-input %s" % dir_output_evaluation) cmd_args.append("--dir-output %s" % dir_output_figures) exe = os.path.abspath( show_evaluated_simulated_stack_similarity.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: ph.print_warning("Visualization of slice similarities failed") # Generate pdfs showing all the side-by-side comparisons cmd_args = [] cmd_args.append("--filenames %s" % (" ").join(filenames_simulated)) cmd_args.append("--dir-output %s" % dir_output_side_by_side) exe = os.path.abspath( export_side_by_side_simulated_vs_original_slice_comparison.__file__) cmd = "python %s %s" % (exe, (" ").join(cmd_args)) exit_code = ph.execute_command(cmd) if exit_code != 0: raise RuntimeError("Generation of PDF overview failed") ph.print_title("Summary") print("Computational Time for Volumetric Reconstruction: %s" % elapsed_time_volrec) print("Computational Time for Pipeline: %s" % ph.stop_timing(time_start)) return 0