def main():
input_parser = InputArgparser(
description="Multiply images. "
"Pixel type is determined by first given image.", )
input_parser.add_filenames(required=True)
input_parser.add_output(required=True)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if len(args.filenames) < 2:
raise IOError("At least two images must be provided")
out_sitk = sitk.ReadImage(args.filenames[0])
for f in args.filenames[1:]:
im_sitk = sitk.Cast(sitk.ReadImage(f), out_sitk.GetPixelIDValue())
out_sitk = out_sitk * im_sitk
dw.DataWriter.write_image(out_sitk, args.output)
if args.verbose:
args.filenames.insert(0, args.output)
ph.show_niftis(args.filenames)
def main():
input_parser = InputArgparser(
description="Show data/slice coverage over specified reconstruction "
"space.", )
input_parser.add_filenames(required=True)
input_parser.add_reconstruction_space(required=True)
input_parser.add_output(required=True)
input_parser.add_dir_input_mc()
input_parser.add_slice_thicknesses()
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
reconstruction_space_sitk = sitk.ReadImage(args.reconstruction_space)
slice_coverage = sc.SliceCoverage(
stacks=stacks,
reconstruction_sitk=reconstruction_space_sitk,
)
slice_coverage.run()
coverage_sitk = slice_coverage.get_coverage_sitk()
dw.DataWriter.write_mask(coverage_sitk, args.output)
if args.verbose:
niftis = [
args.reconstruction_space,
args.output,
]
ph.show_niftis(niftis)
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 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="Perform Bias Field correction using N4ITK.", )
input_parser.add_filename(required=True)
input_parser.add_output(required=True)
input_parser.add_filename_mask()
input_parser.add_option(
option_string="--convergence-threshold",
type=float,
help="Specify the convergence threshold.",
default=1e-6,
)
input_parser.add_option(
option_string="--spline-order",
type=int,
help="Specify the spline order defining the bias field estimate.",
default=3,
)
input_parser.add_option(
option_string="--wiener-filter-noise",
type=float,
help="Specify the noise estimate defining the Wiener filter.",
default=0.11,
)
input_parser.add_option(
option_string="--bias-field-fwhm",
type=float,
help="Specify the full width at half maximum parameter characterizing "
"the width of the Gaussian deconvolution.",
default=0.15,
)
input_parser.add_log_config(default=1)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
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.log_config:
input_parser.log_config(os.path.abspath(__file__))
# Read data
stack = st.Stack.from_filename(
file_path=args.filename,
file_path_mask=args.filename_mask,
extract_slices=False,
)
# Perform Bias Field Correction
# ph.print_title("Perform Bias Field Correction")
bias_field_corrector = n4itk.N4BiasFieldCorrection(
stack=stack,
use_mask=True if args.filename_mask is not None else False,
convergence_threshold=args.convergence_threshold,
spline_order=args.spline_order,
wiener_filter_noise=args.wiener_filter_noise,
bias_field_fwhm=args.bias_field_fwhm,
)
ph.print_info("N4ITK Bias Field Correction ... ", newline=False)
bias_field_corrector.run_bias_field_correction()
stack_corrected = bias_field_corrector.get_bias_field_corrected_stack()
print("done")
dw.DataWriter.write_image(stack_corrected.sitk, args.output)
elapsed_time = ph.stop_timing(time_start)
if args.verbose:
ph.show_niftis([args.filename, args.output])
ph.print_title("Summary")
exe_file_info = os.path.basename(os.path.abspath(__file__)).split(".")[0]
print("%s | Computational Time for Bias Field Correction: %s" %
(exe_file_info, elapsed_time))
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()
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():
# Read input
parser = argparse.ArgumentParser(
description="Resample image.",
prog=None,
epilog="Author: Michael Ebner ([email protected])",
)
parser.add_argument(
"-m",
"--moving",
help="Path to moving image",
type=str,
required=1,
)
parser.add_argument(
"-f",
"--fixed",
help="Path to fixed image. "
"Can be 'same' if fixed image space is identical to the moving image "
"space.",
type=str,
required=1,
)
parser.add_argument(
"-o",
"--output",
help="Path to resampled image",
type=str,
required=1,
)
parser.add_argument(
"-t",
"--transform",
help="Path to (SimpleITK) transformation (.txt) or displacement "
"field (.nii.gz) to be applied",
type=str,
required=0,
)
parser.add_argument(
"-i",
"--interpolator",
help="Interpolator for image resampling. Can be either name (%s) "
"or order (0, 1)" % (", ".join(ALLOWED_INTERPOLATORS)),
type=str,
required=0,
default="Linear",
# default="BSpline", # might cause problems for some images
)
parser.add_argument(
"-p",
"--padding",
help="Padding value",
type=int,
required=0,
default=0,
)
parser.add_argument(
"-s",
"--spacing",
help="Set spacing for resampling grid in fixed image space",
nargs="+",
type=float,
default=None,
)
parser.add_argument(
"-atg",
"--add-to-grid",
help="Additional grid extension/reduction in each direction of each "
"axis in millimeter. If scalar, changes are applied uniformly to grid",
nargs="+",
type=float,
default=None,
)
parser.add_argument(
"-v",
"--verbose",
help="Turn on/off verbose output",
type=int,
required=0,
default=0,
)
args = parser.parse_args()
if args.fixed == "same":
args.fixed = args.moving
resampler = simplereg.resampler.Resampler(
path_to_fixed=args.fixed,
path_to_moving=args.moving,
path_to_transform=args.transform,
interpolator=args.interpolator,
spacing=args.spacing,
padding=args.padding,
add_to_grid=args.add_to_grid,
verbose=args.verbose,
)
resampler.run()
resampler.write_image(args.output)
if args.verbose:
ph.show_niftis([
args.output,
args.fixed,
])
return 0
