def test_estimate_motion(self):
filename = "SRR_reference.nii.gz"
output = os.path.join(self.dir_output, filename)
dir_reference = os.path.join(self.dir_data, "estimate_motion")
dir_reference_mc = os.path.join(dir_reference, "motion_correction")
path_to_reference = os.path.join(dir_reference, filename)
path_to_reference_mask = ph.append_to_filename(
os.path.join(dir_reference, filename), self.suffix_mask)
two_step_cycles = 1
iter_max = 5
exe = os.path.abspath(rsfmri_estimate_motion.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filename %s" % self.filename)
cmd_args.append("--filename-mask %s" % ph.append_to_filename(
self.filename, self.suffix_mask))
cmd_args.append("--dir-output %s" % self.dir_output)
cmd_args.append("--two-step-cycles %s" % two_step_cycles)
cmd_args.append("--iter-max %d" % iter_max)
cmd = (" ").join(cmd_args)
self.assertEqual(ph.execute_command(cmd), 0)
# Check SRR volume
res_sitk = sitkh.read_nifti_image_sitk(output)
ref_sitk = sitkh.read_nifti_image_sitk(path_to_reference)
diff_sitk = res_sitk - ref_sitk
error = np.linalg.norm(sitk.GetArrayFromImage(diff_sitk))
self.assertAlmostEqual(error, 0, places=self.precision)
# Check SRR mask volume
res_sitk = sitkh.read_nifti_image_sitk(
ph.append_to_filename(output, self.suffix_mask))
ref_sitk = sitkh.read_nifti_image_sitk(path_to_reference_mask)
diff_sitk = res_sitk - ref_sitk
error = np.linalg.norm(sitk.GetArrayFromImage(diff_sitk))
self.assertAlmostEqual(error, 0, places=self.precision)
# Check transforms
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
dir_res_mc = os.path.join(self.dir_output, "motion_correction")
trafos_res = sorted(
[os.path.join(dir_res_mc, t)
for t in os.listdir(dir_res_mc) if p.match(t)])
trafos_ref = sorted(
[os.path.join(dir_reference_mc, t)
for t in os.listdir(dir_reference_mc) if p.match(t)])
self.assertEqual(len(trafos_res), len(trafos_ref))
for i in range(len(trafos_ref)):
nda_res = sitkh.read_transform_sitk(trafos_res[i]).GetParameters()
nda_ref = sitkh.read_transform_sitk(trafos_ref[i]).GetParameters()
nda_diff = np.linalg.norm(np.array(nda_res) - nda_ref)
self.assertAlmostEqual(nda_diff, 0, places=self.precision)
def test_compute_write_read_slice_similarities(self):
paths_to_stacks = [
os.path.join(DIR_TEST, "fetal_brain_%d.nii.gz" % d)
for d in range(0, 3)
]
path_to_reference = os.path.join(
DIR_TEST, "FetalBrain_reconstruction_3stacks_myAlg.nii.gz")
stacks = [
st.Stack.from_filename(p, ph.append_to_filename(p, "_mask"))
for p in paths_to_stacks
]
reference = st.Stack.from_filename(path_to_reference,
extract_slices=False)
residual_evaluator = res_ev.ResidualEvaluator(stacks, reference)
residual_evaluator.compute_slice_projections()
residual_evaluator.evaluate_slice_similarities()
residual_evaluator.write_slice_similarities(self.dir_tmp)
slice_similarities = residual_evaluator.get_slice_similarities()
residual_evaluator1 = res_ev.ResidualEvaluator()
residual_evaluator1.read_slice_similarities(self.dir_tmp)
slice_similarities1 = residual_evaluator1.get_slice_similarities()
for stack_name in slice_similarities.keys():
for m in slice_similarities[stack_name].keys():
rho_res = slice_similarities[stack_name][m]
rho_res1 = slice_similarities1[stack_name][m]
error = np.linalg.norm(rho_res - rho_res1)
self.assertAlmostEqual(error, 0, places=self.precision)
def test_compute_write_read_similarities(self):
paths_to_stacks = [
os.path.join(
DIR_TEST, "fetal_brain_%d.nii.gz" % d) for d in range(0, 3)
]
path_to_reference = os.path.join(
DIR_TEST, "FetalBrain_reconstruction_3stacks_myAlg.nii.gz")
reference = st.Stack.from_filename(
path_to_reference, extract_slices=False)
stacks = [
st.Stack.from_filename(p, ph.append_to_filename(p, "_mask"))
for p in paths_to_stacks
]
stacks = [s.get_resampled_stack(reference.sitk) for s in stacks]
residual_evaluator = ise.ImageSimilarityEvaluator(stacks, reference)
residual_evaluator.compute_similarities()
residual_evaluator.write_similarities(DIR_TMP)
similarities = residual_evaluator.get_similarities()
similarities1 = ise.ImageSimilarityEvaluator()
similarities1.read_similarities(DIR_TMP)
similarities1 = similarities1.get_similarities()
for m in residual_evaluator.get_measures():
rho_res = similarities[m]
rho_res1 = similarities1[m]
error = np.linalg.norm(rho_res - rho_res1)
self.assertAlmostEqual(error, 0, places=self.precision)
def test_reconstruct_volume_from_slices(self):
filename = "bold_s2v.nii.gz"
output = os.path.join(self.dir_output, filename)
dir_reference = os.path.join(
self.dir_data, "reconstruct_volume_from_slices")
dir_input_mc = os.path.join(
self.dir_data, "reconstruct_volume_from_slices", "motion_correction")
path_to_reference = os.path.join(dir_reference, filename)
iter_max = 3
alpha = 0.05
cmd_args = []
exe = os.path.abspath(rsfmri_reconstruct_volume_from_slices.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filename %s" % self.filename)
cmd_args.append("--filename-mask %s" % ph.append_to_filename(
self.filename, self.suffix_mask))
cmd_args.append("--dir-input-mc %s" % dir_input_mc)
cmd_args.append("--output %s" % output)
cmd_args.append("--iter-max %d" % iter_max)
cmd_args.append("--alpha %f" % alpha)
cmd = (" ").join(cmd_args)
self.assertEqual(ph.execute_command(cmd), 0)
# Check whether identical reconstruction has been created
reconstruction_sitk = sitkh.read_sitk_vector_image(output)
reference_sitk = sitkh.read_sitk_vector_image(path_to_reference)
difference_sitk = reconstruction_sitk - reference_sitk
error = np.linalg.norm(sitk.GetArrayFromImage(difference_sitk))
self.assertAlmostEqual(error, 0, places=self.precision)
def setUp(self):
self.precision = 7
self.dir_output = os.path.join(DIR_TMP, "reconstruction")
self.dir_data = os.path.join(DIR_TEST, "case-studies", "fetal-brain")
self.filename = "axial"
self.suffix_mask = "_mask"
self.path_to_file = os.path.join(self.dir_data, "input-data",
"%s.nii.gz" % self.filename)
self.filename_recon = "SRR_stacks3_TK1_lsmr_alpha0p02_itermax5.nii.gz"
self.path_to_recon = os.path.join(self.dir_data, "recon_projections",
self.filename_recon)
self.path_to_recon_mask = ph.append_to_filename(
self.path_to_recon, self.suffix_mask)
def test_slice_projections_not_created(self):
paths_to_stacks = [
os.path.join(DIR_TEST, "fetal_brain_%d.nii.gz" % d)
for d in range(0, 1)
]
path_to_reference = os.path.join(
DIR_TEST, "FetalBrain_reconstruction_3stacks_myAlg.nii.gz")
stacks = [
st.Stack.from_filename(p, ph.append_to_filename(p, "_mask"))
for p in paths_to_stacks
]
reference = st.Stack.from_filename(path_to_reference,
extract_slices=False)
residual_evaluator = res_ev.ResidualEvaluator(stacks, reference)
self.assertRaises(
exceptions.ObjectNotCreated,
lambda: residual_evaluator.evaluate_slice_similarities())
def _exe_dilate_masks(self, kernel, paths_to_masks, label=1):
cmd_loop = []
kernel_str = [str(k) for k in kernel]
# Export dilated mask to temp directory
for i_mask, path_to_mask in enumerate(paths_to_masks):
directory = os.path.dirname(path_to_mask)
mask_filename = os.path.basename(path_to_mask)
path_to_mask_dilated = os.path.join(
self._dir_temp, ph.append_to_filename(mask_filename, "_dil"))
cmd_args = ["c3d"]
cmd_args.append(path_to_mask)
cmd_args.append("-dilate %s %smm" % (label, "x".join(kernel_str)))
cmd_args.append("-o %s" % path_to_mask_dilated)
cmd = self._sep.join(cmd_args)
cmd_loop.append(cmd)
paths_to_masks[i_mask] = path_to_mask_dilated
return "\n".join(cmd_loop)
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(required=True)
input_parser.add_output(help="Path to registration transform (.txt)",
required=True)
input_parser.add_fixed_mask(required=False)
input_parser.add_moving_mask(required=False)
input_parser.add_option(
option_string="--initial-transform",
type=str,
help="Path to initial transform. "
"If not provided, registration will be initialized based on "
"rigid alignment of eigenbasis of the fixed/moving image masks "
"using principal component analysis",
default=None)
input_parser.add_v2v_method(
option_string="--method",
help="Registration method used for the registration.",
default="RegAladin",
)
input_parser.add_argument(
"--refine-pca",
"-refine-pca",
action='store_true',
help="If given, PCA-based initializations will be refined using "
"RegAladin registrations.")
input_parser.add_dir_input_mc()
input_parser.add_verbose(default=0)
input_parser.add_log_config(default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if not args.output.endswith(".txt"):
raise IOError("output transformation path must end in '.txt'")
dir_output = os.path.dirname(args.output)
ph.create_directory(dir_output)
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
fixed = st.Stack.from_filename(file_path=args.fixed,
file_path_mask=args.fixed_mask,
extract_slices=False)
moving = st.Stack.from_filename(file_path=args.moving,
file_path_mask=args.moving_mask,
extract_slices=False)
path_to_tmp_output = os.path.join(
DIR_TMP, ph.append_to_filename(os.path.basename(args.moving),
"_warped"))
# ---------------------------- Initialization ----------------------------
if args.initial_transform is None:
ph.print_title("Estimate initial transform using PCA")
if args.moving_mask is None or args.fixed_mask is None:
ph.print_warning("Fixed and moving masks are strongly recommended")
transform_initializer = tinit.TransformInitializer(
fixed=fixed,
moving=moving,
similarity_measure="NMI",
refine_pca_initializations=args.refine_pca,
)
transform_initializer.run()
transform_init_sitk = transform_initializer.get_transform_sitk()
else:
transform_init_sitk = sitkh.read_transform_sitk(args.initial_transform)
sitk.WriteTransform(transform_init_sitk, args.output)
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Registration")
if args.method == "RegAladin":
path_to_transform_regaladin = os.path.join(DIR_TMP,
"transform_regaladin.txt")
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
args.output, path_to_transform_regaladin)
ph.execute_command(cmd, verbose=False)
# Run NiftyReg
cmd_args = ["reg_aladin"]
cmd_args.append("-ref '%s'" % args.fixed)
cmd_args.append("-flo '%s'" % args.moving)
cmd_args.append("-res '%s'" % path_to_tmp_output)
cmd_args.append("-inaff '%s'" % path_to_transform_regaladin)
cmd_args.append("-aff '%s'" % path_to_transform_regaladin)
cmd_args.append("-rigOnly")
cmd_args.append("-ln 2") # seems to perform better for spina bifida
cmd_args.append("-voff")
if args.fixed_mask is not None:
cmd_args.append("-rmask '%s'" % args.fixed_mask)
# To avoid error "0 correspondences between blocks were found" that can
# occur for some cases. Also, disable moving mask, as this would be ignored
# anyway
cmd_args.append("-noSym")
# if args.moving_mask is not None:
# cmd_args.append("-fmask '%s'" % args.moving_mask)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk '%s' '%s'" % (
path_to_transform_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
else:
path_to_transform_flirt = os.path.join(DIR_TMP, "transform_flirt.txt")
# Convert SimpleITK into FLIRT transform
cmd = "simplereg_transform -sitk2flirt '%s' '%s' '%s' '%s'" % (
args.output, args.fixed, args.moving, path_to_transform_flirt)
ph.execute_command(cmd, verbose=False)
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, 180, 180) for x in ["x", "y", "z"]
]
cmd_args = ["flirt"]
cmd_args.append("-in '%s'" % args.moving)
cmd_args.append("-ref '%s'" % args.fixed)
if args.initial_transform is not None:
cmd_args.append("-init '%s'" % path_to_transform_flirt)
cmd_args.append("-omat '%s'" % path_to_transform_flirt)
cmd_args.append("-out '%s'" % path_to_tmp_output)
cmd_args.append("-dof 6")
cmd_args.append((" ").join(search_angles))
if args.moving_mask is not None:
cmd_args.append("-inweight '%s'" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-refweight '%s'" % args.fixed_mask)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert FLIRT to SimpleITK transform
cmd = "simplereg_transform -flirt2sitk '%s' '%s' '%s' '%s'" % (
path_to_transform_flirt, args.fixed, args.moving, args.output)
ph.execute_command(cmd, verbose=False)
if args.dir_input_mc is not None:
ph.print_title("Update Motion-Correction Transformations")
transform_sitk = sitkh.read_transform_sitk(args.output, inverse=1)
if args.dir_input_mc.endswith("/"):
subdir_mc = args.dir_input_mc.split("/")[-2]
else:
subdir_mc = args.dir_input_mc.split("/")[-1]
dir_output_mc = os.path.join(dir_output, subdir_mc)
ph.create_directory(dir_output_mc, delete_files=True)
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
trafos = [t for t in os.listdir(args.dir_input_mc) if p.match(t)]
for t in trafos:
path_to_input_transform = os.path.join(args.dir_input_mc, t)
path_to_output_transform = os.path.join(dir_output_mc, t)
t_sitk = sitkh.read_transform_sitk(path_to_input_transform)
t_sitk = sitkh.get_composite_sitk_affine_transform(
transform_sitk, t_sitk)
sitk.WriteTransform(t_sitk, path_to_output_transform)
ph.print_info("%d transformations written to '%s'" %
(len(trafos), dir_output_mc))
if args.verbose:
ph.show_niftis([args.fixed, path_to_tmp_output])
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
return 0
def test_reconstruct_volume(self):
filename = "SRR_stacks3_TK1_lsmr_alpha0p02_itermax5.nii.gz"
output = os.path.join(self.dir_output, filename)
dir_reference = os.path.join(self.dir_data, "reconstruct_volume")
dir_reference_mc = os.path.join(dir_reference, "motion_correction")
path_to_reference = os.path.join(dir_reference, filename)
path_to_reference_mask = ph.append_to_filename(
os.path.join(dir_reference, filename), self.suffix_mask)
two_step_cycles = 1
iter_max = 5
threshold = 0.75
alpha = 0.02
alpha_first = 0.2
sigma = 1
intensity_correction = 1
isotropic_resolution = 1.02
v2v_method = "RegAladin"
cmd_args = []
cmd_args.append("--filenames %s" % " ".join(self.filenames))
cmd_args.append("--output %s" % output)
cmd_args.append("--suffix-mask %s" % self.suffix_mask)
cmd_args.append("--two-step-cycles %s" % two_step_cycles)
cmd_args.append("--iter-max %d" % iter_max)
cmd_args.append("--threshold-first %f" % threshold)
cmd_args.append("--sigma %f" % sigma)
cmd_args.append("--threshold %f" % threshold)
cmd_args.append("--intensity-correction %d" % intensity_correction)
cmd_args.append("--isotropic-resolution %s" % isotropic_resolution)
cmd_args.append("--alpha %f" % alpha)
cmd_args.append("--alpha-first %f" % alpha_first)
cmd_args.append("--v2v-method %s" % v2v_method)
# cmd_args.append("--verbose 1")
cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args)
self.assertEqual(ph.execute_command(cmd), 0)
# Check SRR volume
res_sitk = sitkh.read_nifti_image_sitk(output)
ref_sitk = sitkh.read_nifti_image_sitk(path_to_reference)
diff_sitk = res_sitk - ref_sitk
error = np.linalg.norm(sitk.GetArrayFromImage(diff_sitk))
self.assertAlmostEqual(error, 0, places=self.precision)
# Check SRR mask volume
res_sitk = sitkh.read_nifti_image_sitk(
ph.append_to_filename(output, "_mask"))
ref_sitk = sitkh.read_nifti_image_sitk(path_to_reference_mask)
diff_sitk = res_sitk - ref_sitk
error = np.linalg.norm(sitk.GetArrayFromImage(diff_sitk))
self.assertAlmostEqual(error, 0, places=self.precision)
# Check transforms
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
dir_res_mc = os.path.join(self.dir_output, "motion_correction")
trafos_res = sorted([
os.path.join(dir_res_mc, t) for t in os.listdir(dir_res_mc)
if p.match(t)
])
trafos_ref = sorted([
os.path.join(dir_reference_mc, t)
for t in os.listdir(dir_reference_mc) if p.match(t)
])
self.assertEqual(len(trafos_res), len(trafos_ref))
for i in range(len(trafos_ref)):
nda_res = sitkh.read_transform_sitk(trafos_res[i]).GetParameters()
nda_ref = sitkh.read_transform_sitk(trafos_ref[i]).GetParameters()
nda_diff = np.linalg.norm(np.array(nda_res) - nda_ref)
self.assertAlmostEqual(nda_diff, 0, places=self.precision)
def test_register_image(self):
filename = "registration_transform_sitk.txt"
gestational_age = 28
path_to_recon = os.path.join(
self.dir_data, "register_image",
"SRR_stacks3_TK1_lsmr_alpha0p02_itermax5.nii.gz")
dir_input_mc = os.path.join(self.dir_data, "register_image",
"motion_correction")
path_to_transform_res = os.path.join(self.dir_output, filename)
path_to_transform_ref = os.path.join(self.dir_data, "register_image",
filename)
dir_ref_mc = os.path.join(self.dir_data, "register_image",
"motion_correction_ref")
path_to_rejected_slices_ref = os.path.join(dir_ref_mc,
"rejected_slices.json")
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 = ["niftymic_register_image"]
cmd_args.append("--fixed %s" % template)
cmd_args.append("--moving %s" % path_to_recon)
cmd_args.append("--fixed-mask %s" % template_mask)
cmd_args.append("--moving-mask %s" %
ph.append_to_filename(path_to_recon, self.suffix_mask))
cmd_args.append("--dir-input-mc %s" % dir_input_mc)
cmd_args.append("--output %s" % path_to_transform_res)
cmd_args.append("--init-pca")
# cmd_args.append("--verbose 1")
self.assertEqual(ph.execute_command(" ".join(cmd_args)), 0)
# Check registration transform
res_sitk = sitkh.read_transform_sitk(path_to_transform_res)
ref_sitk = sitkh.read_transform_sitk(path_to_transform_ref)
res_nda = res_sitk.GetParameters()
ref_nda = ref_sitk.GetParameters()
diff_nda = np.array(res_nda) - ref_nda
self.assertAlmostEqual(np.linalg.norm(diff_nda),
0,
places=self.precision)
# Check individual slice transforms
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
dir_res_mc = os.path.join(self.dir_output, "motion_correction")
trafos_res = sorted([
os.path.join(dir_res_mc, t) for t in os.listdir(dir_res_mc)
if p.match(t)
])
trafos_ref = sorted([
os.path.join(dir_ref_mc, t) for t in os.listdir(dir_res_mc)
if p.match(t)
])
self.assertEqual(len(trafos_res), len(trafos_ref))
for i in range(len(trafos_ref)):
nda_res = sitkh.read_transform_sitk(trafos_res[i]).GetParameters()
nda_ref = sitkh.read_transform_sitk(trafos_ref[i]).GetParameters()
nda_diff = np.linalg.norm(np.array(nda_res) - nda_ref)
self.assertAlmostEqual(nda_diff, 0, places=self.precision)
# Check rejected_slices.json
path_to_rejected_slices_res = os.path.join(dir_res_mc,
"rejected_slices.json")
self.assertEqual(ph.file_exists(path_to_rejected_slices_res), True)
rejected_slices_res = ph.read_dictionary_from_json(
path_to_rejected_slices_res)
rejected_slices_ref = ph.read_dictionary_from_json(
path_to_rejected_slices_ref)
self.assertEqual(rejected_slices_res == rejected_slices_ref, True)
def main():
time_start = ph.start_timing()
flag_individual_cases_only = 1
flag_batch_script = 0
batch_ctr = [32]
flag_correct_bias_field = 0
# flag_correct_intensities = 0
flag_collect_segmentations = 0
flag_select_images_segmentations = 0
flag_reconstruct_volume_subject_space = 0
flag_reconstruct_volume_subject_space_irtk = 0
flag_reconstruct_volume_subject_space_show_comparison = 0
flag_register_to_template = 0
flag_register_to_template_irtk = 0
flag_show_srr_template_space = 0
flag_reconstruct_volume_template_space = 0
flag_collect_volumetric_reconstruction_results = 0
flag_show_volumetric_reconstruction_results = 0
flag_rsync_stuff = 0
# Analysis
flag_remove_failed_cases_for_analysis = 1
flag_postop = 2 # 0... preop, 1...postop, 2... pre+postop
flag_evaluate_image_similarities = 0
flag_analyse_image_similarities = 1
flag_evaluate_slice_residual_similarities = 0
flag_analyse_slice_residual_similarities = 0
flag_analyse_stacks = 0
flag_analyse_qualitative_assessment = 0
flag_collect_data_blinded_analysis = 0
flag_anonymize_data_blinded_analysis = 0
provide_comparison = 0
intensity_correction = 1
isotropic_resolution = 0.75
alpha = 0.02
outlier_rejection = 1
threshold = 0.7
threshold_first = 0.6
# metric = "ANTSNeighborhoodCorrelation"
# metric_radius = 5
# multiresolution = 0
prefix_srr = "srr_"
prefix_srr_qa = "masked_"
# ----------------------------------Set Up---------------------------------
if flag_correct_bias_field:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT, "BiasFieldCorrection")
elif flag_reconstruct_volume_subject_space:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionSubjectSpace")
elif flag_register_to_template:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionRegisterToTemplate")
elif flag_reconstruct_volume_template_space:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT,
"VolumetricReconstructionTemplateSpace")
else:
dir_batch = os.path.join(utils.DIR_BATCH_ROOT, "foo")
file_prefix_batch = os.path.join(dir_batch, "command")
if flag_batch_script:
verbose = 0
else:
verbose = 1
data_reader = dr.ExcelSheetDataReader(utils.EXCEL_FILE)
data_reader.read_data()
cases = data_reader.get_data()
if flag_analyse_qualitative_assessment:
data_reader = dr.ExcelSheetQualitativeAssessmentReader(utils.QA_FILE)
data_reader.read_data()
qualitative_assessment = data_reader.get_data()
statistical_evaluation = se.StatisticalEvaluation(
qualitative_assessment)
statistical_evaluation.run_tests(ref="seg_manual")
ph.exit()
cases_similarities = []
cases_stacks = []
if flag_individual_cases_only:
N_cases = len(INDIVIDUAL_CASE_IDS)
else:
N_cases = len(cases.keys())
i_case = 0
for case_id in sorted(cases.keys()):
if flag_individual_cases_only and case_id not in INDIVIDUAL_CASE_IDS:
continue
if not flag_analyse_image_similarities and \
not flag_analyse_slice_residual_similarities:
i_case += 1
ph.print_title("%d/%d: %s" % (i_case, N_cases, case_id))
if flag_rsync_stuff:
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="template_space", seg_mode="")
dir_input = re.sub("Volumes/spina/",
"Volumes/medic-volumetric_res/SpinaBifida/",
dir_output)
cmd = "rsync -avuhn --exclude 'motion_correction' %sseg_manual %s" % (
dir_input, dir_output)
ph.print_execution(cmd)
# ph.execute_command(cmd)
# -------------------------Correct Bias Field--------------------------
if flag_correct_bias_field:
filenames = utils.get_filenames_preprocessing_bias_field(case_id)
paths_to_filenames = [
os.path.join(utils.get_directory_case_original(case_id), f)
for f in filenames
]
dir_output = utils.get_directory_case_preprocessing(
case_id, stage="01_N4ITK")
# no image found matching the pattern
if len(paths_to_filenames) == 0:
continue
cmd_args = []
cmd_args.append("--filenames %s" % " ".join(paths_to_filenames))
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--prefix-output ''")
cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
# # Skip case in case segmentations have not been provided yet
# if not ph.directory_exists(utils.get_directory_case_segmentation(
# case_id, utils.SEGMENTATION_INIT, SEG_MODES[0])):
# continue
# ------------------------Collect Segmentations------------------------
if flag_collect_segmentations:
# Skip case in case segmentations have been collected already
if ph.directory_exists(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, SEG_MODES[0])):
ph.print_info("skipped")
continue
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_INIT)
for i_seg_mode, seg_mode in enumerate(SEG_MODES):
directory_selected = utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, seg_mode)
ph.create_directory(directory_selected)
paths_to_filenames_init = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_INIT, seg_mode), f)
for f in filenames
]
paths_to_filenames_selected = [
os.path.join(directory_selected, f) for f in filenames
]
for i in range(len(filenames)):
cmd = "cp -p %s %s" % (paths_to_filenames_init[i],
paths_to_filenames_selected[i])
# ph.print_execution(cmd)
ph.execute_command(cmd)
if flag_select_images_segmentations:
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_SELECTED)
paths_to_filenames = [
os.path.join(
utils.get_directory_case_preprocessing(case_id,
stage="01_N4ITK"),
f) for f in filenames
]
paths_to_filenames_masks = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, "seg_manual"), f)
for f in filenames
]
for i in range(len(filenames)):
ph.show_niftis(
[paths_to_filenames[i]],
segmentation=paths_to_filenames_masks[i],
# viewer="fsleyes",
)
ph.pause()
ph.killall_itksnap()
# # -------------------------Correct Intensities-----------------------
# if flag_correct_intensities:
# filenames = utils.get_segmented_image_filenames(case_id)
# paths_to_filenames_bias = [os.path.join(
# utils.get_directory_case_preprocessing(
# case_id, stage="01_N4ITK"), f) for f in filenames]
# print paths_to_filenames_bias
# -----------------Reconstruct Volume in Subject Space-----------------
if flag_reconstruct_volume_subject_space:
filenames = utils.get_segmented_image_filenames(
case_id, subfolder=utils.SEGMENTATION_SELECTED)
# filenames = filenames[0:2]
paths_to_filenames = [
os.path.join(
utils.get_directory_case_preprocessing(case_id,
stage="01_N4ITK"),
f) for f in filenames
]
# Estimate target stack
target_stack_index = utils.get_target_stack_index(
case_id, utils.SEGMENTATION_SELECTED, "seg_auto", filenames)
for i, seg_mode in enumerate(SEG_MODES):
# Get mask filenames
paths_to_filenames_masks = [
os.path.join(
utils.get_directory_case_segmentation(
case_id, utils.SEGMENTATION_SELECTED, seg_mode), f)
for f in filenames
]
if flag_reconstruct_volume_subject_space_irtk:
if seg_mode != "seg_manual":
continue
utils.export_irtk_call_to_workstation(
case_id=case_id,
filenames=filenames,
seg_mode=seg_mode,
isotropic_resolution=isotropic_resolution,
target_stack_index=target_stack_index,
kernel_mask_dilation=(15, 15, 4))
else:
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode)
# dir_output = "/tmp/foo"
cmd_args = []
cmd_args.append("--filenames %s" %
" ".join(paths_to_filenames))
cmd_args.append("--filenames-masks %s" %
" ".join(paths_to_filenames_masks))
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--use-masks-srr 0")
cmd_args.append("--isotropic-resolution %f" %
isotropic_resolution)
cmd_args.append("--target-stack-index %d" %
target_stack_index)
cmd_args.append("--intensity-correction %d" %
intensity_correction)
cmd_args.append("--outlier-rejection %d" %
outlier_rejection)
cmd_args.append("--threshold-first %f" % threshold_first)
cmd_args.append("--threshold %f" % threshold)
# cmd_args.append("--metric %s" % metric)
# cmd_args.append("--multiresolution %d" % multiresolution)
# cmd_args.append("--metric-radius %s" % metric_radius)
# if i > 0:
# cmd_args.append("--reconstruction-space %s" % (
# utils.get_path_to_recon(
# utils.get_directory_case_recon_seg_mode(
# case_id, "seg_manual"))))
# cmd_args.append("--two-step-cycles 0")
cmd_args.append("--verbose %d" % verbose)
cmd_args.append("--provide-comparison %d" %
provide_comparison)
# cmd_args.append("--iter-max 1")
cmd = "niftymic_reconstruct_volume %s" % (
" ").join(cmd_args)
ph.execute_command(
cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
if flag_reconstruct_volume_subject_space_show_comparison:
recon_paths = []
for seg_mode in SEG_MODES:
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
recon_paths.append(path_to_recon)
recon_path_irtk = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode="IRTK"), "IRTK_SRR.nii.gz")
show_modes = list(SEG_MODES)
if ph.file_exists(recon_path_irtk):
recon_paths.append(recon_path_irtk)
show_modes.append("irtk")
ph.show_niftis(recon_paths)
ph.print_info("Sequence: %s" % (" -- ").join(show_modes))
ph.pause()
ph.killall_itksnap()
# -------------------------Register to template------------------------
if flag_register_to_template:
for seg_mode in SEG_MODES:
cmd_args = []
# register seg_auto-recon to template space
if seg_mode == "seg_auto":
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
ph.append_to_filename(path_to_recon, "_mask"))
path_to_reference = \
template_stack_estimator.get_path_to_template()
dir_input_motion_correction = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# dir_output = "/home/mebner/tmp"
# # ------- DELETE -----
# dir_output = re.sub("data", "foo+1", dir_output)
# dir_output = re.sub(
# "volumetric_reconstruction/20180126/template_space/seg_auto",
# "", dir_output)
# # -------
# cmd_args.append("--use-fixed-mask 1")
cmd_args.append("--use-moving-mask 1")
# HACK
path_to_initial_transform = os.path.join(
utils.DIR_INPUT_ROOT_DATA, case_id,
"volumetric_reconstruction", "20180126",
"template_space", "seg_manual",
"registration_transform_sitk.txt")
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
# register remaining recons to registered seg_auto-recon
else:
path_to_reference = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_auto"),
suffix="ResamplingToTemplateSpace",
)
path_to_initial_transform = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_auto"),
"registration_transform_sitk.txt")
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode))
dir_input_motion_correction = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
cmd_args.append("--use-fixed-mask 0")
cmd_args.append("--use-moving-mask 0")
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
cmd_args.append("--moving %s" % path_to_recon)
cmd_args.append("--fixed %s" % path_to_reference)
cmd_args.append("--dir-input %s" % dir_input_motion_correction)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--write-transform 1")
cmd_args.append("--verbose %d" % verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
if flag_register_to_template_irtk:
dir_input = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="subject_space", seg_mode="IRTK")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id, recon_space="template_space", seg_mode="IRTK")
path_to_recon = os.path.join(dir_input, "IRTK_SRR.nii.gz")
path_to_reference = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_manual"),
suffix="ResamplingToTemplateSpace",
)
path_to_initial_transform = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="seg_manual"), "registration_transform_sitk.txt")
cmd_args = []
cmd_args.append("--fixed %s" % path_to_reference)
cmd_args.append("--moving %s" % path_to_recon)
cmd_args.append("--initial-transform %s" %
path_to_initial_transform)
cmd_args.append("--use-fixed-mask 0")
cmd_args.append("--use-moving-mask 0")
cmd_args.append("--use-flirt 0")
cmd_args.append("--use-regaladin 1")
cmd_args.append("--test-ap-flip 0")
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--verbose %d" % verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
ph.execute_command(cmd)
if flag_show_srr_template_space:
recon_paths = []
show_modes = list(SEG_MODES)
# show_modes.append("IRTK")
for seg_mode in show_modes:
dir_input = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# # ------- DELETE -----
# dir_input = re.sub("data", "foo+1", dir_input)
# dir_input = re.sub(
# "volumetric_reconstruction/20180126/template_space/seg_auto",
# "", dir_input)
# # -------
path_to_recon_space = utils.get_path_to_recon(
dir_input,
suffix="ResamplingToTemplateSpace",
)
recon_paths.append(path_to_recon_space)
ph.show_niftis(recon_paths)
ph.print_info("Sequence: %s" % (" -- ").join(show_modes))
ph.pause()
ph.killall_itksnap()
# -----------------Reconstruct Volume in Template Space----------------
if flag_reconstruct_volume_template_space:
for seg_mode in SEG_MODES:
path_to_recon_space = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode),
suffix="ResamplingToTemplateSpace",
)
dir_input = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode), "motion_correction")
dir_output = utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode)
# dir_output = os.path.join("/tmp/spina/template_space/%s-%s" % (
# case_id, seg_mode))
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--reconstruction-space %s" %
path_to_recon_space)
cmd_args.append("--alpha %s" % alpha)
cmd_args.append("--verbose %s" % verbose)
cmd_args.append("--use-masks-srr 0")
# cmd_args.append("--minimizer L-BFGS-B")
# cmd_args.append("--alpha 0.006")
# cmd_args.append("--reconstruction-type HuberL2")
# cmd_args.append("--data-loss arctan")
# cmd_args.append("--iterations 5")
# cmd_args.append("--data-loss-scale 0.7")
cmd = "niftymic_reconstruct_volume_from_slices %s" % \
(" ").join(cmd_args)
ph.execute_command(cmd,
flag_print_to_file=flag_batch_script,
path_to_file="%s%d.txt" %
(file_prefix_batch, ph.add_one(batch_ctr)))
# ----------------Collect SRR results in Template Space----------------
if flag_collect_volumetric_reconstruction_results:
directory = utils.get_directory_case_recon_summary(case_id)
ph.create_directory(directory)
# clear potentially existing files
cmd = "rm -f %s/*.nii.gz" % (directory)
ph.execute_command(cmd)
# Collect SRRs
for seg_mode in SEG_MODES:
path_to_recon_src = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode), )
path_to_recon = os.path.join(
directory, "%s%s.nii.gz" % (prefix_srr, seg_mode))
cmd = "cp -p %s %s" % (path_to_recon_src, path_to_recon)
ph.execute_command(cmd)
# Collect IRTK recon
path_to_recon_src = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode="IRTK"),
"IRTK_SRR_LinearResamplingToTemplateSpace.nii.gz")
path_to_recon = os.path.join(directory,
"%s%s.nii.gz" % (prefix_srr, "irtk"))
cmd = "cp -p %s %s" % (path_to_recon_src, path_to_recon)
ph.execute_command(cmd)
# Collect evaluation mask
path_to_recon = utils.get_path_to_recon(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="subject_space",
seg_mode="seg_auto"))
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
ph.append_to_filename(path_to_recon, "_mask"))
path_to_template = \
template_stack_estimator.get_path_to_template()
path_to_template_mask_src = ph.append_to_filename(
path_to_template, "_mask_dil")
path_to_template_mask = "%s/" % directory
cmd = "cp -p %s %s" % (path_to_template_mask_src,
path_to_template_mask)
ph.execute_command(cmd)
if flag_show_volumetric_reconstruction_results:
dir_output = utils.get_directory_case_recon_summary(case_id)
paths_to_recons = []
for seg_mode in RECON_MODES:
path_to_recon = os.path.join(
dir_output, "%s%s.nii.gz" % (prefix_srr, seg_mode))
paths_to_recons.append(path_to_recon)
path_to_mask = "%s/STA*.nii.gz" % dir_output
cmd = ph.show_niftis(paths_to_recons, segmentation=path_to_mask)
sitkh.write_executable_file([cmd], dir_output=dir_output)
ph.pause()
ph.killall_itksnap()
# ---------------------Evaluate Image Similarities---------------------
if flag_evaluate_image_similarities:
dir_input = utils.get_directory_case_recon_summary(case_id)
dir_output = utils.get_directory_case_recon_similarities(case_id)
paths_to_recons = []
for seg_mode in ["seg_auto", "detect", "irtk"]:
path_to_recon = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, seg_mode))
paths_to_recons.append(path_to_recon)
path_to_reference = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, "seg_manual"))
path_to_reference_mask = utils.get_path_to_mask(dir_input)
cmd_args = []
cmd_args.append("--filenames %s" % " ".join(paths_to_recons))
cmd_args.append("--reference %s" % path_to_reference)
cmd_args.append("--reference-mask %s" % path_to_reference_mask)
# cmd_args.append("--verbose 1")
cmd_args.append("--dir-output %s" % dir_output)
exe = re.sub("pyc", "py",
os.path.abspath(evaluate_image_similarity.__file__))
cmd_args.insert(0, exe)
# clear potentially existing files
cmd = "rm -f %s/*.txt" % (dir_output)
ph.execute_command(cmd)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
# -----------------Evaluate Slice Residual Similarities----------------
if flag_evaluate_slice_residual_similarities:
path_to_reference_mask = utils.get_path_to_mask(
utils.get_directory_case_recon_summary(case_id))
dir_output_root = \
utils.get_directory_case_slice_residual_similarities(case_id)
# clear potentially existing files
# cmd = "rm -f %s/*.txt" % (dir_output_root)
# ph.execute_command(cmd)
for seg_mode in SEG_MODES:
dir_input = os.path.join(
utils.get_directory_case_recon_seg_mode(
case_id=case_id,
recon_space="template_space",
seg_mode=seg_mode,
), "motion_correction")
path_to_reference = os.path.join(
utils.get_directory_case_recon_summary(case_id),
"%s%s.nii.gz" % (prefix_srr, seg_mode))
dir_output = os.path.join(dir_output_root, seg_mode)
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--reference %s" % path_to_reference)
cmd_args.append("--reference-mask %s" % path_to_reference_mask)
cmd_args.append("--use-reference-mask 1")
cmd_args.append("--use-slice-masks 0")
# cmd_args.append("--verbose 1")
cmd_args.append("--dir-output %s" % dir_output)
exe = re.sub("pyc", "py", os.path.abspath(esrs.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
# Collect data for blinded analysis
if flag_collect_data_blinded_analysis:
if flag_remove_failed_cases_for_analysis and case_id in RECON_FAILED_CASE_IDS:
continue
dir_input = utils.get_directory_case_recon_summary(case_id)
# pattern = "STA([0-9]+)[_]mask.nii.gz"
pattern = "STA([0-9]+)[_]mask_dil.nii.gz"
p = re.compile(pattern)
gw = [
p.match(f).group(1) for f in os.listdir(dir_input)
if p.match(f)
][0]
dir_output = os.path.join(
utils.get_directory_blinded_analysis(case_id, "open"), case_id)
exe = re.sub("pyc", "py", os.path.abspath(mswm.__file__))
recons = []
for seg_mode in RECON_MODES:
path_to_recon = os.path.join(
dir_input, "%s%s.nii.gz" % (prefix_srr, seg_mode))
cmd_args = []
cmd_args.append("--filename %s" % path_to_recon)
cmd_args.append("--gestational-age %s" % gw)
cmd_args.append("--dir-output %s" % dir_output)
cmd_args.append("--prefix-output %s" % prefix_srr_qa)
cmd_args.append("--verbose 0")
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
# ph.execute_command(cmd)
recon = "%s%s" % (prefix_srr_qa,
os.path.basename(path_to_recon))
recons.append(recon)
ph.write_show_niftis_exe(recons, dir_output)
if flag_anonymize_data_blinded_analysis:
dir_input = os.path.join(
utils.get_directory_blinded_analysis(case_id, "open"), case_id)
dir_output_dictionaries = utils.get_directory_anonymized_dictionares(
case_id)
dir_output_anonymized_images = os.path.join(
utils.get_directory_blinded_analysis(case_id, "anonymized"),
case_id)
if not ph.directory_exists(dir_input):
continue
ph.create_directory(dir_output_dictionaries)
ph.create_directory(dir_output_anonymized_images)
data_anonymizer = da.DataAnonymizer()
# Create random dictionary (only required once)
# data_anonymizer.set_prefix_identifiers("%s_" % case_id)
# data_anonymizer.read_nifti_filenames_from_directory(dir_input)
# data_anonymizer.generate_identifiers()
# data_anonymizer.generate_randomized_dictionary()
# data_anonymizer.write_dictionary(
# dir_output_dictionaries, "dictionary_%s" % case_id)
# Read dictionary
data_anonymizer.read_dictionary(dir_output_dictionaries,
"dictionary_%s" % case_id)
# Anonymize files
if 0:
ph.clear_directory(dir_output_anonymized_images)
data_anonymizer.anonymize_files(dir_input,
dir_output_anonymized_images)
# Write executable script
filenames = [
"%s.nii.gz" % f
for f in sorted(data_anonymizer.get_identifiers())
]
ph.write_show_niftis_exe(filenames,
dir_output_anonymized_images)
# Reveal anonymized files
if 1:
filenames = data_anonymizer.reveal_anonymized_files(
dir_output_anonymized_images)
filenames = sorted(["%s" % f for f in filenames])
ph.write_show_niftis_exe(filenames,
dir_output_anonymized_images)
# Reveal additional, original files
# data_anonymizer.reveal_original_files(dir_output)
# relative_directory = re.sub(
# utils.get_directory_blinded_analysis(case_id, "anonymized"),
# ".",
# dir_output_anonymized_images)
# paths_to_filenames = [os.path.join(
# relative_directory, f) for f in filenames]
# ---------------------Analyse Image Similarities---------------------
if flag_analyse_image_similarities or \
flag_analyse_slice_residual_similarities or \
flag_analyse_stacks:
if flag_remove_failed_cases_for_analysis:
if case_id in RECON_FAILED_CASE_IDS:
continue
if cases[case_id]['postrep'] == flag_postop or flag_postop == 2:
cases_similarities.append(case_id)
cases_stacks.append(
utils.get_segmented_image_filenames(
case_id,
# subfolder=utils.SEGMENTATION_INIT,
subfolder=utils.SEGMENTATION_SELECTED,
))
dir_output_analysis = os.path.join(
# "/Users/mebner/UCL/UCL/Publications",
"/home/mebner/Dropbox/UCL/Publications",
"2018_MICCAI/brain_reconstruction_paper")
if flag_analyse_image_similarities:
dir_inputs = []
filename = "image_similarities_postop%d.txt" % flag_postop
for case_id in cases_similarities:
dir_inputs.append(
utils.get_directory_case_recon_similarities(case_id))
cmd_args = []
cmd_args.append("--dir-inputs %s" % " ".join(dir_inputs))
cmd_args.append("--dir-output %s" % dir_output_analysis)
cmd_args.append("--filename %s" % filename)
exe = re.sub("pyc", "py",
os.path.abspath(src.analyse_image_similarities.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
ph.execute_command(cmd)
if flag_analyse_slice_residual_similarities:
dir_inputs = []
filename = "slice_residuals_postop%d.txt" % flag_postop
for case_id in cases_similarities:
dir_inputs.append(
utils.get_directory_case_slice_residual_similarities(case_id))
cmd_args = []
cmd_args.append("--dir-inputs %s" % " ".join(dir_inputs))
cmd_args.append("--subfolder %s" % " ".join(SEG_MODES))
cmd_args.append("--dir-output %s" % dir_output_analysis)
cmd_args.append("--filename %s" % filename)
exe = re.sub(
"pyc", "py",
os.path.abspath(src.analyse_slice_residual_similarities.__file__))
cmd_args.insert(0, exe)
cmd = "python %s" % " ".join(cmd_args)
# print len(cases_similarities)
# print cases_similarities
ph.execute_command(cmd)
if flag_analyse_stacks:
cases_stacks_N = [len(s) for s in cases_stacks]
ph.print_subtitle("%d cases -- Number of stacks" % len(cases_stacks))
ph.print_info("min: %g" % np.min(cases_stacks_N))
ph.print_info("mean: %g" % np.mean(cases_stacks_N))
ph.print_info("median: %g" % np.median(cases_stacks_N))
ph.print_info("max: %g" % np.max(cases_stacks_N))
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Pipeline: %s" % (elapsed_time))
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(required=True)
input_parser.add_output(help="Path to registration transform (.txt)",
required=True)
input_parser.add_fixed_mask()
input_parser.add_moving_mask()
input_parser.add_dir_input_mc()
input_parser.add_search_angle(default=180)
input_parser.add_option(option_string="--initial-transform",
type=str,
help="Path to initial transform.",
default=None)
input_parser.add_option(
option_string="--test-ap-flip",
type=int,
help="Turn on/off functionality to run an additional registration "
"after an AP-flip. Seems to be more robust to find a better "
"registration outcome in general.",
default=1)
input_parser.add_option(
option_string="--use-flirt",
type=int,
help="Turn on/off functionality to use FLIRT for the registration.",
default=1)
input_parser.add_option(
option_string="--use-regaladin",
type=int,
help="Turn on/off functionality to use RegAladin for the "
"registration.",
default=1)
input_parser.add_verbose(default=0)
input_parser.add_log_config(default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
debug = 0
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if not args.use_regaladin and not args.use_flirt:
raise IOError("Either RegAladin or FLIRT must be activated.")
if not args.output.endswith(".txt"):
raise IOError("output transformation path must end in '.txt'")
dir_output = os.path.dirname(args.output)
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
fixed = st.Stack.from_filename(file_path=args.fixed,
file_path_mask=args.fixed_mask,
extract_slices=False)
moving = st.Stack.from_filename(file_path=args.moving,
file_path_mask=args.moving_mask,
extract_slices=False)
if args.initial_transform is not None:
transform_sitk = sitkh.read_transform_sitk(args.initial_transform)
else:
transform_sitk = sitk.AffineTransform(fixed.sitk.GetDimension())
sitk.WriteTransform(transform_sitk, args.output)
path_to_tmp_output = os.path.join(
DIR_TMP, ph.append_to_filename(os.path.basename(args.moving),
"_warped"))
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Register Reconstruction to Template")
if args.use_flirt:
path_to_transform_flirt = os.path.join(DIR_TMP, "transform_flirt.txt")
# Convert SimpleITK into FLIRT transform
cmd = "simplereg_transform -sitk2flirt %s %s %s %s" % (
args.output, args.fixed, args.moving, path_to_transform_flirt)
ph.execute_command(cmd, verbose=False)
# 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"]
]
# flt = nipype.interfaces.fsl.FLIRT()
# flt.inputs.in_file = args.moving
# flt.inputs.reference = args.fixed
# if args.initial_transform is not None:
# flt.inputs.in_matrix_file = path_to_transform_flirt
# flt.inputs.out_matrix_file = path_to_transform_flirt
# # flt.inputs.output_type = "NIFTI_GZ"
# flt.inputs.out_file = path_to_tmp_output
# flt.inputs.args = "-dof 6"
# flt.inputs.args += " %s" % " ".join(search_angles)
# if args.moving_mask is not None:
# flt.inputs.in_weight = args.moving_mask
# if args.fixed_mask is not None:
# flt.inputs.ref_weight = args.fixed_mask
# ph.print_info("Run Registration (FLIRT) ... ", newline=False)
# flt.run()
# print("done")
cmd_args = ["flirt"]
cmd_args.append("-in %s" % args.moving)
cmd_args.append("-ref %s" % args.fixed)
if args.initial_transform is not None:
cmd_args.append("-init %s" % path_to_transform_flirt)
cmd_args.append("-omat %s" % path_to_transform_flirt)
cmd_args.append("-out %s" % path_to_tmp_output)
cmd_args.append("-dof 6")
cmd_args.append((" ").join(search_angles))
if args.moving_mask is not None:
cmd_args.append("-inweight %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-refweight %s" % args.fixed_mask)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert FLIRT to SimpleITK transform
cmd = "simplereg_transform -flirt2sitk %s %s %s %s" % (
path_to_transform_flirt, args.fixed, args.moving, args.output)
ph.execute_command(cmd, verbose=False)
if debug:
ph.show_niftis([args.fixed, path_to_tmp_output])
# Additionally, use RegAladin for more accurate alignment
# Rationale: FLIRT has better capture range, but RegAladin seems to
# find better alignment once it is within its capture range.
if args.use_regaladin:
path_to_transform_regaladin = os.path.join(DIR_TMP,
"transform_regaladin.txt")
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
args.output, path_to_transform_regaladin)
ph.execute_command(cmd, verbose=False)
# nreg = nipype.interfaces.niftyreg.RegAladin()
# nreg.inputs.ref_file = args.fixed
# nreg.inputs.flo_file = args.moving
# nreg.inputs.res_file = path_to_tmp_output
# nreg.inputs.in_aff_file = path_to_transform_regaladin
# nreg.inputs.aff_file = path_to_transform_regaladin
# nreg.inputs.args = "-rigOnly -voff"
# if args.moving_mask is not None:
# nreg.inputs.fmask_file = args.moving_mask
# if args.fixed_mask is not None:
# nreg.inputs.rmask_file = args.fixed_mask
# ph.print_info("Run Registration (RegAladin) ... ", newline=False)
# nreg.run()
# print("done")
cmd_args = ["reg_aladin"]
cmd_args.append("-ref %s" % args.fixed)
cmd_args.append("-flo %s" % args.moving)
cmd_args.append("-res %s" % path_to_tmp_output)
if args.initial_transform is not None or args.use_flirt == 1:
cmd_args.append("-inaff %s" % path_to_transform_regaladin)
cmd_args.append("-aff %s" % path_to_transform_regaladin)
# cmd_args.append("-cog")
# cmd_args.append("-ln 2")
cmd_args.append("-rigOnly")
cmd_args.append("-voff")
if args.moving_mask is not None:
cmd_args.append("-fmask %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-rmask %s" % args.fixed_mask)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk %s %s" % (
path_to_transform_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
if debug:
ph.show_niftis([args.fixed, path_to_tmp_output])
if args.test_ap_flip:
path_to_transform_flip = os.path.join(DIR_TMP, "transform_flip.txt")
path_to_tmp_output_flip = os.path.join(DIR_TMP, "output_flip.nii.gz")
# Get AP-flip transform
transform_ap_flip_sitk = get_ap_flip_transform(args.fixed)
path_to_transform_flip_regaladin = os.path.join(
DIR_TMP, "transform_flip_regaladin.txt")
sitk.WriteTransform(transform_ap_flip_sitk, path_to_transform_flip)
# Compose current transform with AP flip transform
cmd = "simplereg_transform -c %s %s %s" % (
args.output, path_to_transform_flip, path_to_transform_flip)
ph.execute_command(cmd, verbose=False)
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
path_to_transform_flip, path_to_transform_flip_regaladin)
ph.execute_command(cmd, verbose=False)
# nreg = nipype.interfaces.niftyreg.RegAladin()
# nreg.inputs.ref_file = args.fixed
# nreg.inputs.flo_file = args.moving
# nreg.inputs.res_file = path_to_tmp_output_flip
# nreg.inputs.in_aff_file = path_to_transform_flip_regaladin
# nreg.inputs.aff_file = path_to_transform_flip_regaladin
# nreg.inputs.args = "-rigOnly -voff"
# if args.moving_mask is not None:
# nreg.inputs.fmask_file = args.moving_mask
# if args.fixed_mask is not None:
# nreg.inputs.rmask_file = args.fixed_mask
# ph.print_info("Run Registration AP-flipped (RegAladin) ... ",
# newline=False)
# nreg.run()
# print("done")
cmd_args = ["reg_aladin"]
cmd_args.append("-ref %s" % args.fixed)
cmd_args.append("-flo %s" % args.moving)
cmd_args.append("-res %s" % path_to_tmp_output_flip)
cmd_args.append("-inaff %s" % path_to_transform_flip_regaladin)
cmd_args.append("-aff %s" % path_to_transform_flip_regaladin)
cmd_args.append("-rigOnly")
# cmd_args.append("-ln 2")
cmd_args.append("-voff")
if args.moving_mask is not None:
cmd_args.append("-fmask %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-rmask %s" % args.fixed_mask)
ph.print_info("Run Registration AP-flipped (RegAladin) ... ",
newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
if debug:
ph.show_niftis(
[args.fixed, path_to_tmp_output, path_to_tmp_output_flip])
warped_moving = st.Stack.from_filename(path_to_tmp_output,
extract_slices=False)
warped_moving_flip = st.Stack.from_filename(path_to_tmp_output_flip,
extract_slices=False)
fixed = st.Stack.from_filename(args.fixed, args.fixed_mask)
stacks = [warped_moving, warped_moving_flip]
image_similarity_evaluator = ise.ImageSimilarityEvaluator(
stacks=stacks, reference=fixed)
image_similarity_evaluator.compute_similarities()
similarities = image_similarity_evaluator.get_similarities()
if similarities["NMI"][1] > similarities["NMI"][0]:
ph.print_info("AP-flipped outcome better")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk %s %s" % (
path_to_transform_flip_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
# Copy better outcome
cmd = "cp -p %s %s" % (path_to_tmp_output_flip, path_to_tmp_output)
ph.execute_command(cmd, verbose=False)
else:
ph.print_info("AP-flip does not improve outcome")
if args.dir_input_mc is not None:
transform_sitk = sitkh.read_transform_sitk(args.output, inverse=1)
if args.dir_input_mc.endswith("/"):
subdir_mc = args.dir_input_mc.split("/")[-2]
else:
subdir_mc = args.dir_input_mc.split("/")[-1]
dir_output_mc = os.path.join(dir_output, subdir_mc)
ph.create_directory(dir_output_mc, delete_files=True)
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
trafos = [t for t in os.listdir(args.dir_input_mc) if p.match(t)]
for t in trafos:
path_to_input_transform = os.path.join(args.dir_input_mc, t)
path_to_output_transform = os.path.join(dir_output_mc, t)
t_sitk = sitkh.read_transform_sitk(path_to_input_transform)
t_sitk = sitkh.get_composite_sitk_affine_transform(
transform_sitk, t_sitk)
sitk.WriteTransform(t_sitk, path_to_output_transform)
if args.verbose:
ph.show_niftis([args.fixed, path_to_tmp_output])
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
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 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 "
"motion-corrected (or static) stacks of low-resolution slices.", )
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks()
input_parser.add_dir_input_mc()
input_parser.add_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack(default=None)
input_parser.add_extra_frame_target(default=10)
input_parser.add_isotropic_resolution(default=None)
input_parser.add_intensity_correction(default=1)
input_parser.add_reconstruction_space(default=None)
input_parser.add_minimizer(default="lsmr")
input_parser.add_iter_max(default=10)
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_data_loss(default="linear")
input_parser.add_data_loss_scale(default=1)
input_parser.add_alpha(default=0.01 # TK1L2
# default=0.006 #TVL2, HuberL2
)
input_parser.add_rho(default=0.5)
input_parser.add_tv_solver(default="PD")
input_parser.add_pd_alg_type(default="ALG2")
input_parser.add_iterations(default=15)
input_parser.add_log_config(default=1)
input_parser.add_use_masks_srr(default=0)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_verbose(default=0)
input_parser.add_viewer(default="itksnap")
input_parser.add_argument(
"--mask",
"-mask",
action='store_true',
help="If given, input images are interpreted as image masks. "
"Obtained volumetric reconstruction will be exported in uint8 format.")
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.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]:
raise IOError("Reconstruction type unknown")
if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]):
raise ValueError("output filename '%s' invalid; "
"allowed image extensions are: %s" %
(args.output, ", ".join(ALLOWED_EXTENSIONS)))
dir_output = os.path.dirname(args.output)
ph.create_directory(dir_output)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if args.verbose:
show_niftis = []
# show_niftis = [f for f in args.filenames]
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
if args.mask:
filenames_masks = args.filenames
else:
filenames_masks = args.filenames_masks
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=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 and not args.mask:
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())
# -------------------------Volumetric Reconstruction-----------------------
ph.print_title("Volumetric Reconstruction")
# Reconstruction space is given isotropically resampled target stack
if args.reconstruction_space is None:
recon0 = stacks[target_stack_index].get_isotropically_resampled_stack(
resolution=args.isotropic_resolution,
extra_frame=args.extra_frame_target)
recon0 = recon0.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",
)
# Reconstruction space was provided by user
else:
recon0 = st.Stack.from_filename(args.reconstruction_space,
extract_slices=False)
# Change resolution for isotropic resolution if provided by user
if args.isotropic_resolution is not None:
recon0 = recon0.get_isotropically_resampled_stack(
args.isotropic_resolution)
# Use image information of selected target stack as recon0 serves
# as initial value for reconstruction
recon0 = stacks[target_stack_index].get_resampled_stack(recon0.sitk)
recon0 = recon0.get_stack_multiplied_with_mask()
ph.print_info("Reconstruction space defined with %s mm3 resolution" %
" x ".join(["%.2f" % s for s in recon0.sitk.GetSpacing()]))
if args.sda:
ph.print_title("Compute SDA reconstruction")
SDA = sda.ScatteredDataApproximation(stacks,
recon0,
sigma=args.alpha,
sda_mask=args.mask)
SDA.run()
recon = SDA.get_reconstruction()
if args.mask:
dw.DataWriter.write_mask(recon.sitk_mask, args.output)
else:
dw.DataWriter.write_image(recon.sitk, args.output)
if args.verbose:
show_niftis.insert(0, args.output)
else:
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute Initial value for %s" %
args.reconstruction_type)
SRR0 = tk.TikhonovSolver(
stacks=stacks,
reconstruction=recon0,
alpha=args.alpha,
iter_max=np.min([5, args.iter_max]),
reg_type="TK1",
minimizer="lsmr",
data_loss="linear",
use_masks=args.use_masks_srr,
# verbose=args.verbose,
)
else:
ph.print_title("Compute %s reconstruction" %
args.reconstruction_type)
SRR0 = tk.TikhonovSolver(
stacks=stacks,
reconstruction=recon0,
alpha=args.alpha,
iter_max=args.iter_max,
reg_type="TK1",
minimizer=args.minimizer,
data_loss=args.data_loss,
data_loss_scale=args.data_loss_scale,
use_masks=args.use_masks_srr,
# verbose=args.verbose,
)
SRR0.run()
recon = SRR0.get_reconstruction()
if args.reconstruction_type in ["TVL2", "HuberL2"]:
output = ph.append_to_filename(args.output, "_initTK1L2")
else:
output = args.output
if args.mask:
mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk)
mask_estimator.run()
mask_sitk = mask_estimator.get_mask_sitk()
dw.DataWriter.write_mask(mask_sitk, output)
else:
dw.DataWriter.write_image(recon.sitk, output)
if args.verbose:
show_niftis.insert(0, output)
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute %s reconstruction" %
args.reconstruction_type)
if args.tv_solver == "ADMM":
SRR = admm.ADMMSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(
SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
rho=args.rho,
data_loss=args.data_loss,
iterations=args.iterations,
use_masks=args.use_masks_srr,
verbose=args.verbose,
)
else:
SRR = pd.PrimalDualSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(
SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
iterations=args.iterations,
alg_type=args.pd_alg_type,
reg_type="TV"
if args.reconstruction_type == "TVL2" else "huber",
data_loss=args.data_loss,
use_masks=args.use_masks_srr,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
if args.mask:
mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk)
mask_estimator.run()
mask_sitk = mask_estimator.get_mask_sitk()
dw.DataWriter.write_mask(mask_sitk, args.output)
else:
dw.DataWriter.write_image(recon.sitk, args.output)
if args.verbose:
show_niftis.insert(0, args.output)
if args.verbose:
ph.show_niftis(show_niftis, viewer=args.viewer)
ph.print_line_separator()
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Volumetric Reconstruction: %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()
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()
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
