def to_mni_xfm(xfm):
s = Stages()
defs = xfm.newname_with_suffix("_defs", subdir="tmp")
s.add(CmdStage(cmd=["transformix", "-def", "all",
"-out", defs.dir,
"-tp", xfm.path,
"-xfm", os.path.join(defs.filename_wo_ext, defs.ext)],
inputs=(xfm,), outputs=(defs,)))
out_xfm = s.defer(itk.itk_convert_xfm(defs, out_ext=".mnc"))
return Result(stages=s, output=out_xfm)
def convert(infile : ImgAtom, out_ext : str) -> Result[ImgAtom]:
s = Stages()
outfile = infile.newext(ext=out_ext)
if infile.mask is not None:
outfile.mask = s.defer(convert(infile.mask, out_ext=out_ext))
if infile.labels is not None:
outfile.mask = s.defer(convert(infile.labels, out_ext=out_ext))
s.add(CmdStage(inputs=(infile,), outputs=(outfile,),
cmd = ['c3d', infile.path, '-o', outfile.path]))
return Result(stages=s, output=outfile)
def average_images(
imgs: Sequence[ImgAtom],
dimensions: int = 3,
normalize: bool = False,
output_dir: str = '.',
name_wo_ext: str = "average",
out_ext: Optional[str] = None,
avg_file: Optional[ITKImgAtom] = None) -> Result[ITKImgAtom]:
s = Stages()
if len(imgs) == 0:
raise ValueError(
"`AverageImages` arg `imgs` is empty (can't average zero files)")
ext = out_ext or imgs[0].ext
# the output_dir basically gives us the equivalent of the pipeline_sub_dir for
# regular input files to a pipeline, so use that here
avg = avg_file or ImgAtom(name=os.path.join(output_dir,
'%s.todo' % name_wo_ext),
orig_name=None,
pipeline_sub_dir=output_dir)
avg.ext = ext
# if all input files have masks associated with them, add the combined mask to
# the average:
# TODO what if avg_file has a mask ... should that be used instead? (then rename avg -> avg_file above)
all_inputs_have_masks = all((img.mask for img in imgs))
if all_inputs_have_masks:
combined_mask = (ImgAtom(name=os.path.join(
avg_file.dir, '%s_mask.todo' % avg_file.filename_wo_ext),
orig_name=None,
pipeline_sub_dir=avg_file.pipeline_sub_dir)
if avg_file is not None else ImgAtom(
name=os.path.join(output_dir, '%s_mask.todo' %
name_wo_ext),
orig_name=None,
pipeline_sub_dir=output_dir))
combined_mask.ext = ext
s.defer(
max(imgs=sorted({img_inst.mask
for img_inst in imgs}),
out_img=combined_mask))
avg.mask = combined_mask
s.add(
CmdStage(
inputs=imgs,
outputs=(avg, ),
cmd=["AverageImages",
str(dimensions), avg.path,
"%d" % normalize] + [img.path for img in imgs]))
return Result(stages=s, output=avg)
def convert(infile: ImgAtom, out_ext: str) -> Result[ImgAtom]:
s = Stages()
outfile = infile.newext(ext=out_ext)
if infile.mask is not None:
outfile.mask = s.defer(convert(infile.mask, out_ext=out_ext))
if infile.labels is not None:
outfile.mask = s.defer(convert(infile.labels, out_ext=out_ext))
s.add(
CmdStage(inputs=(infile, ),
outputs=(outfile, ),
cmd=['c3d', infile.path, '-o', outfile.path]))
return Result(stages=s, output=outfile)
def dramms_warp_simple(img: NiiAtom,
xfm: DrammsXfmAtom,
like: Optional[NiiAtom] = None,
extra_flags: Tuple[str] = (),
use_nn_interpolation=None,
invert: bool = False,
new_name_wo_ext: str = None,
subdir: str = None) -> Result[NiiAtom]:
"""
Resample an image, ignoring mask/labels
...
new_name_wo_ext -- string indicating a user specified file name (without extension)
"""
s = Stages()
if invert:
inv_xfm = xfm.newname_with_suffix("_inverted")
s.add(
CmdStage(cmd=["dramms-defop", "-i", xfm.path, inv_xfm.path],
inputs=(xfm, ),
outputs=(inv_xfm, )))
xfm = inv_xfm
if not subdir:
subdir = 'resampled'
if not new_name_wo_ext:
# FIXME the path to `outf` is wrong. For instance, resampling a mask file ends up in the initial model
# FIXME directory instead of in the resampled directory.
# FIXME At the same time, `like.newname(...)` doesn't work either, for similar reasons.
# FIXME Not clear if there's a general "automagic" fix for this.
# FIXME Also, using the xfm's filename is wrong, since we might be resampling, e.g., a mask.
# FIXME We should basically use the same naming scheme as is used to generate the xfm's name but
# FIXME use the files for resampling, not registration
outf = img.newname(name=xfm.filename_wo_ext + '-resampled',
subdir=subdir)
else:
# we have the output filename without extension. This should replace the entire
# current "base" of the filename.
outf = img.newname(name=new_name_wo_ext, subdir=subdir)
stage = CmdStage(
inputs=(xfm, like, img),
outputs=(outf, ),
cmd=(['dramms-warp'] + (['-n'] if use_nn_interpolation else []) +
(['-transform %s' % xfm.path]) #if xfm is not identity else [])
+ ['-like %s' % like.path, img.path, outf.path]))
s.add(stage)
return Result(stages=s, output=outf)
def average_transforms(xfms, avg_xfm):
intermediate_xfm = avg_xfm.newname_with_suffix("_inter", subdir="tmp")
s = Stages()
s.add(CmdStage(cmd=["echo", ('(Transform "WeightedCombinationTransform")\n'
'(SubTransforms %s)\n'
'(NormalizeCombinationsWeights "true")\n') %
' '.join(sorted(xfm.path for xfm in xfms))],
inputs=xfms, outputs=(intermediate_xfm,)))
s.add(CmdStage(cmd=["transformix", "-def", "all",
"-out", os.path.dirname(avg_xfm.path),
"-tp", intermediate_xfm.path,
"-xfm", avg_xfm.path],
inputs=(intermediate_xfm,), outputs=(avg_xfm,)))
def to_mni_xfm(xfm):
s = Stages()
defs = xfm.newname_with_suffix("_defs", subdir="tmp")
s.add(
CmdStage(cmd=[
"transformix", "-def", "all", "-out", defs.dir, "-tp",
xfm.path, "-xfm",
os.path.join(defs.filename_wo_ext, defs.ext)
],
inputs=(xfm, ),
outputs=(defs, )))
out_xfm = s.defer(itk.itk_convert_xfm(defs, out_ext=".mnc"))
return Result(stages=s, output=out_xfm)
def average_images(imgs : Sequence[ImgAtom],
dimensions : int = 3,
normalize : bool = False,
output_dir : str = '.',
name_wo_ext : str = "average",
out_ext : Optional[str] = None,
avg_file : Optional[ITKImgAtom] = None) -> Result[ITKImgAtom]:
s = Stages()
if len(imgs) == 0:
raise ValueError("`AverageImages` arg `imgs` is empty (can't average zero files)")
ext = out_ext or imgs[0].ext
# the output_dir basically gives us the equivalent of the pipeline_sub_dir for
# regular input files to a pipeline, so use that here
avg = avg_file or ImgAtom(name=os.path.join(output_dir, '%s.todo' % name_wo_ext),
orig_name=None,
pipeline_sub_dir=output_dir)
avg.ext = ext
# if all input files have masks associated with them, add the combined mask to
# the average:
# TODO what if avg_file has a mask ... should that be used instead? (then rename avg -> avg_file above)
all_inputs_have_masks = all((img.mask for img in imgs))
if all_inputs_have_masks:
combined_mask = (ImgAtom(name=os.path.join(avg_file.dir, '%s_mask.todo' % avg_file.filename_wo_ext),
orig_name=None,
pipeline_sub_dir=avg_file.pipeline_sub_dir)
if avg_file is not None else
ImgAtom(name=os.path.join(output_dir, '%s_mask.todo' % name_wo_ext),
orig_name=None,
pipeline_sub_dir=output_dir))
combined_mask.ext = ext
s.defer(max(imgs=sorted({img_inst.mask for img_inst in imgs}),
out_img=combined_mask))
avg.mask = combined_mask
s.add(CmdStage(inputs = imgs,
outputs = (avg,),
cmd = ["AverageImages", str(dimensions), avg.path, "%d" % normalize]
+ [img.path for img in imgs]))
return Result(stages=s, output=avg)
def average_transforms(xfms, avg_xfm):
intermediate_xfm = avg_xfm.newname_with_suffix("_inter", subdir="tmp")
s = Stages()
s.add(
CmdStage(cmd=[
"echo",
('(Transform "WeightedCombinationTransform")\n'
'(SubTransforms %s)\n'
'(NormalizeCombinationsWeights "true")\n') %
' '.join(sorted(xfm.path for xfm in xfms))
],
inputs=xfms,
outputs=(intermediate_xfm, )))
s.add(
CmdStage(cmd=[
"transformix", "-def", "all", "-out",
os.path.dirname(avg_xfm.path), "-tp", intermediate_xfm.path,
"-xfm", avg_xfm.path
],
inputs=(intermediate_xfm, ),
outputs=(avg_xfm, )))
def shift_modify_header(img: MincAtom,
shifted_img: MincAtom,
newx: float,
newy: float,
newz: float):
s = Stages()
#Copy file to new location
stage = CmdStage(inputs=(img,), outputs=(shifted_img,), memory=1,
cmd=['cp', img.path, shifted_img.path])
print(stage.render())
s.add(stage)
#Alter header of copied image to shift
xspace_start = 'xspace:start='+newx
yspace_start = 'yspace:start='+newy
zspace_start = 'zspace:start='+newz
stage = CmdStage(inputs=(shifted_img,), outputs=(shifted_img,), memory=1,
cmd=['minc_modify_header','-dinsert',xspace_start,
'-dinsert',yspace_start,
'-dinsert',zspace_start,
shifted_img.path])
print(stage.render())
s.add(stage)
#Alter header of copied image with header modification
append_history_string = ':history= >>> copy and shift: '+ shifted_img.path +' to '+ shifted_img.path
stage = CmdStage(inputs=(shifted_img,), outputs=(shifted_img,), memory=1,
cmd=['minc_modify_header','-sappend',
append_history_string,
shifted_img.path])
print(stage.render())
s.add(stage)
return Result(stages=s, output=shifted_img)
def register(
source: MincAtom,
target: MincAtom,
conf: ANTSConf,
initial_source_transform: Optional[XfmAtom] = None,
transform_name_wo_ext: str = None,
generation: int = None,
resample_source: bool = False,
#resample_name_wo_ext: Optional[str] = None,
resample_subdir: str = "resampled"
) -> Result[XfmHandler]:
"""
...
transform_name_wo_ext -- to use for the output transformation (without the extension)
generation -- if provided, the transformation name will be:
source.filename_wo_ext + "_ANTS_nlin-" + generation
resample_source -- whether or not to resample the source file
Construct a single call to ANTS.
Also does blurring according to the specified options
since the cost function might use these.
"""
s = Stages()
if initial_source_transform is not None:
raise ValueError("ANTs doesn't accept an initial transform")
# if we resample the source, and place it in the "tmp" directory, we should do
# the same with the transformation that is created:
trans_output_dir = "transforms"
if resample_source and resample_subdir == "tmp":
trans_output_dir = "tmp"
if transform_name_wo_ext:
name = os.path.join(source.pipeline_sub_dir, source.output_sub_dir,
trans_output_dir,
"%s.xfm" % (transform_name_wo_ext))
elif generation is not None:
name = os.path.join(
source.pipeline_sub_dir, source.output_sub_dir,
trans_output_dir,
"%s_ANTS_nlin-%s.xfm" % (source.filename_wo_ext, generation))
else:
name = os.path.join(
source.pipeline_sub_dir, source.output_sub_dir,
trans_output_dir, "%s_ANTS_to_%s.xfm" %
(source.filename_wo_ext, target.filename_wo_ext))
out_xfm = XfmAtom(name=name,
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
similarity_cmds = [] # type: List[str]
similarity_inputs = set() # type: Set[MincAtom]
# TODO: similarity_inputs should be a set, but `MincAtom`s aren't hashable
for sim_metric_conf in conf.sim_metric_confs:
if sim_metric_conf.use_gradient_image:
if sim_metric_conf.blur is not None:
gradient_blur_resolution = sim_metric_conf.blur
elif conf.file_resolution is not None:
gradient_blur_resolution = conf.file_resolution
else:
gradient_blur_resolution = None
raise ValueError(
"A similarity metric in the ANTS configuration "
"wants to use the gradients, but I know neither the file resolution nor "
"an intended nonnegative blur fwhm.")
if gradient_blur_resolution <= 0:
warnings.warn(
"Not blurring the gradients as this was explicitly disabled"
)
src = s.defer(mincblur(source,
fwhm=gradient_blur_resolution)).gradient
dest = s.defer(mincblur(
target, fwhm=gradient_blur_resolution)).gradient
else:
# these are not gradient image terms; only blur if explicitly specified:
if sim_metric_conf.blur is not None and sim_metric_conf.blur > 0:
src = s.defer(mincblur(source,
fwhm=sim_metric_conf.blur)).img
dest = s.defer(mincblur(source,
fwhm=sim_metric_conf.blur)).img
else:
src = source
dest = target
similarity_inputs.add(src)
similarity_inputs.add(dest)
inner = ','.join([
src.path, dest.path,
str(sim_metric_conf.weight),
str(sim_metric_conf.radius_or_bins)
])
subcmd = "'" + "".join([sim_metric_conf.metric, '[', inner, ']'
]) + "'"
similarity_cmds.extend(["-m", subcmd])
stage = CmdStage(
inputs=(source, target) + tuple(similarity_inputs) +
cast(tuple, ((source.mask, ) if source.mask else ())),
# need to cast to tuple due to mypy bug; see mypy/issues/622
outputs=(out_xfm, ),
cmd=['ANTS', '3', '--number-of-affine-iterations', '0'] +
similarity_cmds + [
'-t', conf.transformation_model, '-r', conf.regularization,
'-i', conf.iterations, '-o', out_xfm.path
] + (['-x', source.mask.path]
if conf.use_mask and source.mask else []))
# see comments re: mincblur memory configuration
stage.when_runnable_hooks.append(lambda st: set_memory(
st, source=source, conf=conf, mem_cfg=default_ANTS_mem_cfg))
s.add(stage)
resampled = (
s.defer(
mincresample(
img=source,
xfm=out_xfm,
like=target,
interpolation=Interpolation.sinc,
#new_name_wo_ext=resample_name_wo_ext,
subdir=resample_subdir)) if resample_source else None
) # type: Optional[MincAtom]
return Result(stages=s,
output=XfmHandler(source=source,
target=target,
xfm=out_xfm,
resampled=resampled))
def register(source: MincAtom,
target: MincAtom,
conf: ANTSConf,
initial_source_transform: Optional[XfmAtom] = None,
transform_name_wo_ext: str = None,
generation: int = None,
resample_source: bool = False,
#resample_name_wo_ext: Optional[str] = None,
resample_subdir: str = "resampled") -> Result[XfmHandler]:
"""
...
transform_name_wo_ext -- to use for the output transformation (without the extension)
generation -- if provided, the transformation name will be:
source.filename_wo_ext + "_ANTS_nlin-" + generation
resample_source -- whether or not to resample the source file
Construct a single call to ANTS.
Also does blurring according to the specified options
since the cost function might use these.
"""
s = Stages()
if initial_source_transform is not None:
raise ValueError("ANTs doesn't accept an initial transform")
# if we resample the source, and place it in the "tmp" directory, we should do
# the same with the transformation that is created:
trans_output_dir = "transforms"
if resample_source and resample_subdir == "tmp":
trans_output_dir = "tmp"
if transform_name_wo_ext:
name = os.path.join(source.pipeline_sub_dir, source.output_sub_dir, trans_output_dir,
"%s.xfm" % (transform_name_wo_ext))
elif generation is not None:
name = os.path.join(source.pipeline_sub_dir, source.output_sub_dir, trans_output_dir,
"%s_ANTS_nlin-%s.xfm" % (source.filename_wo_ext, generation))
else:
name = os.path.join(source.pipeline_sub_dir, source.output_sub_dir, trans_output_dir,
"%s_ANTS_to_%s.xfm" % (source.filename_wo_ext, target.filename_wo_ext))
out_xfm = XfmAtom(name=name, pipeline_sub_dir=source.pipeline_sub_dir, output_sub_dir=source.output_sub_dir)
similarity_cmds = [] # type: List[str]
similarity_inputs = set() # type: Set[MincAtom]
# TODO: similarity_inputs should be a set, but `MincAtom`s aren't hashable
for sim_metric_conf in conf.sim_metric_confs:
if sim_metric_conf.use_gradient_image:
if sim_metric_conf.blur is not None:
gradient_blur_resolution = sim_metric_conf.blur
elif conf.file_resolution is not None:
gradient_blur_resolution = conf.file_resolution
else:
gradient_blur_resolution = None
raise ValueError("A similarity metric in the ANTS configuration "
"wants to use the gradients, but I know neither the file resolution nor "
"an intended nonnegative blur fwhm.")
if gradient_blur_resolution <= 0:
warnings.warn("Not blurring the gradients as this was explicitly disabled")
src = s.defer(mincblur(source, fwhm=gradient_blur_resolution)).gradient
dest = s.defer(mincblur(target, fwhm=gradient_blur_resolution)).gradient
else:
# these are not gradient image terms; only blur if explicitly specified:
if sim_metric_conf.blur is not None and sim_metric_conf.blur > 0:
src = s.defer(mincblur(source, fwhm=sim_metric_conf.blur)).img
dest = s.defer(mincblur(source, fwhm=sim_metric_conf.blur)).img
else:
src = source
dest = target
similarity_inputs.add(src)
similarity_inputs.add(dest)
inner = ','.join([src.path, dest.path,
str(sim_metric_conf.weight), str(sim_metric_conf.radius_or_bins)])
subcmd = "'" + "".join([sim_metric_conf.metric, '[', inner, ']']) + "'"
similarity_cmds.extend(["-m", subcmd])
stage = CmdStage(
inputs=(source, target) + tuple(similarity_inputs) + cast(tuple, ((source.mask,) if source.mask else ())),
# need to cast to tuple due to mypy bug; see mypy/issues/622
outputs=(out_xfm,),
cmd=['ANTS', '3',
'--number-of-affine-iterations', '0']
+ similarity_cmds
+ ['-t', conf.transformation_model,
'-r', conf.regularization,
'-i', conf.iterations,
'-o', out_xfm.path]
+ (['-x', source.mask.path] if conf.use_mask and source.mask else []))
# see comments re: mincblur memory configuration
stage.when_runnable_hooks.append(lambda st: set_memory(st, source=source, conf=conf,
mem_cfg=default_ANTS_mem_cfg))
s.add(stage)
resampled = (s.defer(mincresample(img=source, xfm=out_xfm, like=target,
interpolation=Interpolation.sinc,
#new_name_wo_ext=resample_name_wo_ext,
subdir=resample_subdir))
if resample_source else None) # type: Optional[MincAtom]
return Result(stages=s,
output=XfmHandler(source=source,
target=target,
xfm=out_xfm,
resampled=resampled))
def antsRegistration(source: MincAtom,
target: MincAtom,
conf: ANTSRegistrationConf,
initial_source_transform: Optional[XfmAtom] = None,
initial_target_transform: Optional[XfmAtom] = None,
# TODO create source_to_target_transform_name_wo_ext, target_to_source_...
transform_name_wo_ext: Optional[str] = None,
generation: Optional[int] = None,
subdir: Optional[str] = None,
resample_source: bool = False,
resample_target: bool = False,
resample_subdir: str = 'tmp'):
"""
:param source: fixedImage
:param target: movingImage
:param conf:
:param initial_source_transform: for --initial-fixed-transform
:param initial_target_transform: for --initial-moving-transform
:param transform_name_wo_ext: name of the target_to_source transform will be based on this
:param generation:
:param subdir:
:param resample_source:
:return:
"""
s = Stages()
source_subdir = subdir if subdir is not None else 'transforms'
# Deal with the transformations first. This function will return two XfmHandlers. One
# from source to target, and one in the other direction.
if transform_name_wo_ext:
xfm_source_to_target = XfmAtom(name=os.path.join(source.pipeline_sub_dir,
source.output_sub_dir,
source_subdir,
"%s.xfm" % transform_name_wo_ext),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
elif generation is not None:
xfm_source_to_target = XfmAtom(name=os.path.join(source.pipeline_sub_dir,
source.output_sub_dir,
source_subdir,
"%s_antsR_to_%s_nlin_%s.xfm" %
(source.filename_wo_ext,
target.filename_wo_ext,
generation)),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
else:
xfm_source_to_target = XfmAtom(name=os.path.join(source.pipeline_sub_dir,
source.output_sub_dir,
source_subdir,
"%s_antsR_to_%s.xfm" %
(source.filename_wo_ext,
target.filename_wo_ext)),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
# model the target_to_source in a similar manner. Given that
# antsRegistration will be passed the "output prefix" for the transform,
# being the whole filename with .xfm, this transform will live in a
# directory belonging to the source image.
# TODO: is this what we want? perhaps we actually want to move this transformation
# over to a subdirectory of the target file...
# N.B.: the name of the following file is forced by antsRegistration; we can't specify a subdir or anything...
# DON'T CHANGE THIS!
xfm_target_to_source = xfm_source_to_target.newname_with_suffix("_inverse", subdir=source_subdir)
# outputs from antRegistration are:
# {output_prefix}_grid_0.mnc
# {output_prefix}.xfm
# {output_prefix}_inverse_grid_0.mnc
# {output_prefix}_inverse.xfm
# Outputs:
# 1) transform from source_to_target
# 2) transform from target_to_source --> need to create an XfmHandler for this one
def optional(x, f, default=[]):
return f(x) if x is not None else []
# TODO: use a proper configuration to set the parameters
# TODO: add a second metric for the gradients (and get gradient files)
if any(m.use_gradient_image for m in conf.metrics):
if conf.file_resolution is None:
raise ValueError("A similarity metric in the ANTS configuration "
"wants to use the gradients, but the file resolution for the "
"configuration has not been set.")
blurred_source, blurred_target = [s.defer(mincblur(img, fwhm=conf.file_resolution)).gradient
for img in (source, target)]
else:
blurred_source = blurred_target = None
def render_metric(m : Metric):
if m.use_gradient_image:
if conf.file_resolution is None:
raise ValueError("A similarity metric in the ANTS configuration "
"wants to use the gradients, but the file resolution for the "
"configuration has not been set.")
fixed = blurred_source
moving = blurred_target
else:
fixed = source
moving = target
return "'%s[%s,%s,%s,%s]'" % (m.metric, fixed.path, moving.path, m.weight, m.radius_or_bins)
if conf.use_masks:
if source.mask is not None and target.mask is not None:
mask_arr = ['--masks', '[%s,%s]' % (source.mask.path, target.mask.path)]
elif source.mask is not None:
mask_arr = ['--masks', '[%s]' % source.mask.path]
elif target.mask is not None:
warnings.warn("only target mask is specified; antsRegistration needs at least a source mask")
mask_arr = []
else:
warnings.warn("no masks supplied")
mask_arr = []
else:
mask_arr = []
cmd = CmdStage(
inputs=tuple(img for img in
(source, target,
source.mask, target.mask,
blurred_source, blurred_target,
initial_source_transform, initial_target_transform)
if img is not None),
outputs=(xfm_source_to_target, xfm_target_to_source),
cmd=['antsRegistration']
+ optional(conf.dimensionality, lambda d: ['--dimensionality', "%d" % d])
+ ['--convergence', render_convergence_conf(conf.convergence)]
+ ['--verbose']
+ ['--minc']
+ ['--collapse-output-transforms', '1']
+ ['--write-composite-transform']
+ ['--winsorize-image-intensities', '[0.01,0.99]']
+ optional(conf.use_histogram_matching, lambda _: ['--use-histogram-matching', '1'])
+ ['--float', '0']
+ ['--output', '[' + os.path.join(xfm_source_to_target.dir, xfm_source_to_target.filename_wo_ext) + ']']
+ ['--transform', conf.transformation_model]
+ optional(initial_source_transform, lambda xfm: ['--initial-fixed-transform', xfm.path])
+ optional(initial_target_transform, lambda xfm: ['--initial-moving-transform', xfm.path])
+ flatten(*[['--metric', render_metric(m)] for m in conf.metrics])
+ mask_arr
+ ['--shrink-factors', 'x'.join(str(s) for s in conf.shrink_factors)]
+ ['--smoothing-sigmas', 'x'.join(str(s) for s in conf.smoothing_sigmas)]
)
# shamelessly stolen from ANTS, probably inaccurate
# see comments re: mincblur memory configuration
def set_memory(st, mem_cfg):
# see comments re: mincblur memory configuration
voxels = reduce(mul, volumeFromFile(source.path).getSizes())
mem_per_voxel = (mem_cfg.mem_per_voxel_coarse
if 0 in conf.convergence.iterations[-1:] #-2?
# yikes ... this parsing should be done earlier
else mem_cfg.mem_per_voxel_fine)
st.setMem(mem_cfg.base_mem + voxels * mem_per_voxel)
cmd.when_runnable_hooks.append(lambda st: set_memory(st, mem_cfg=default_ANTSRegistration_mem_cfg))
s.add(cmd)
# create file names for the two output files. It's better to use our standard
# mincresample command for this, because that also deals with any associated
# masks, whereas antsRegistration would only resample the input files.
resampled_source = (s.defer(mincresample(img=source,
xfm=xfm_source_to_target,
like=target,
interpolation=Interpolation.sinc,
subdir=resample_subdir))
if resample_source else None)
resampled_target = (s.defer(mincresample(img=target,
xfm=xfm_target_to_source,
like=source,
interpolation=Interpolation.sinc,
subdir=resample_subdir))
if resample_target else None)
# return an XfmHandler for both the forward and the inverse transformations
return Result(stages=s,
output=XfmHandler(source=source,
target=target,
xfm=xfm_source_to_target,
resampled=resampled_source,
inverse=XfmHandler(source=target,
target=source,
xfm=xfm_target_to_source,
resampled=resampled_target)))
def antsRegistration(
source: MincAtom,
target: MincAtom,
conf: ANTSRegistrationConf,
initial_source_transform: Optional[XfmAtom] = None,
initial_target_transform: Optional[XfmAtom] = None,
# TODO create source_to_target_transform_name_wo_ext, target_to_source_...
transform_name_wo_ext: Optional[str] = None,
generation: Optional[int] = None,
subdir: Optional[str] = None,
resample_source: bool = False,
resample_target: bool = False,
resample_subdir: str = 'tmp'):
"""
:param source: fixedImage
:param target: movingImage
:param conf:
:param initial_source_transform: for --initial-fixed-transform
:param initial_target_transform: for --initial-moving-transform
:param transform_name_wo_ext: name of the target_to_source transform will be based on this
:param generation:
:param subdir:
:param resample_source:
:return:
"""
s = Stages()
source_subdir = subdir if subdir is not None else 'transforms'
# Deal with the transformations first. This function will return two XfmHandlers. One
# from source to target, and one in the other direction.
if transform_name_wo_ext:
xfm_source_to_target = XfmAtom(
name=os.path.join(source.pipeline_sub_dir, source.output_sub_dir,
source_subdir, "%s.xfm" % transform_name_wo_ext),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
elif generation is not None:
xfm_source_to_target = XfmAtom(
name=os.path.join(
source.pipeline_sub_dir, source.output_sub_dir, source_subdir,
"%s_antsR_to_%s_nlin_%s.xfm" %
(source.filename_wo_ext, target.filename_wo_ext, generation)),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
else:
xfm_source_to_target = XfmAtom(
name=os.path.join(
source.pipeline_sub_dir, source.output_sub_dir, source_subdir,
"%s_antsR_to_%s.xfm" %
(source.filename_wo_ext, target.filename_wo_ext)),
pipeline_sub_dir=source.pipeline_sub_dir,
output_sub_dir=source.output_sub_dir)
# model the target_to_source in a similar manner. Given that
# antsRegistration will be passed the "output prefix" for the transform,
# being the whole filename with .xfm, this transform will live in a
# directory belonging to the source image.
# TODO: is this what we want? perhaps we actually want to move this transformation
# over to a subdirectory of the target file...
# N.B.: the name of the following file is forced by antsRegistration; we can't specify a subdir or anything...
# DON'T CHANGE THIS!
xfm_target_to_source = xfm_source_to_target.newname_with_suffix(
"_inverse", subdir=source_subdir)
# outputs from antRegistration are:
# {output_prefix}_grid_0.mnc
# {output_prefix}.xfm
# {output_prefix}_inverse_grid_0.mnc
# {output_prefix}_inverse.xfm
# Outputs:
# 1) transform from source_to_target
# 2) transform from target_to_source --> need to create an XfmHandler for this one
def optional(x, f, default=[]):
return f(x) if x is not None else []
# TODO: use a proper configuration to set the parameters
# TODO: add a second metric for the gradients (and get gradient files)
if any(m.use_gradient_image for m in conf.metrics):
if conf.file_resolution is None:
raise ValueError(
"A similarity metric in the ANTS configuration "
"wants to use the gradients, but the file resolution for the "
"configuration has not been set.")
blurred_source, blurred_target = [
s.defer(mincblur(img, fwhm=conf.file_resolution)).gradient
for img in (source, target)
]
else:
blurred_source = blurred_target = None
def render_metric(m: Metric):
if m.use_gradient_image:
if conf.file_resolution is None:
raise ValueError(
"A similarity metric in the ANTS configuration "
"wants to use the gradients, but the file resolution for the "
"configuration has not been set.")
fixed = blurred_source
moving = blurred_target
else:
fixed = source
moving = target
return "'%s[%s,%s,%s,%s]'" % (m.metric, fixed.path, moving.path,
m.weight, m.radius_or_bins)
if conf.use_masks:
if source.mask is not None and target.mask is not None:
mask_arr = [
'--masks',
'[%s,%s]' % (source.mask.path, target.mask.path)
]
elif source.mask is not None:
mask_arr = ['--masks', '[%s]' % source.mask.path]
elif target.mask is not None:
warnings.warn(
"only target mask is specified; antsRegistration needs at least a source mask"
)
mask_arr = []
else:
warnings.warn("no masks supplied")
mask_arr = []
else:
mask_arr = []
cmd = CmdStage(
inputs=tuple(img for img in (source, target, source.mask, target.mask,
blurred_source, blurred_target,
initial_source_transform,
initial_target_transform)
if img is not None),
outputs=(xfm_source_to_target, xfm_target_to_source),
cmd=['antsRegistration'] +
optional(conf.dimensionality,
lambda d: ['--dimensionality', "%d" % d]) +
['--convergence',
render_convergence_conf(conf.convergence)] + ['--verbose'] +
['--minc'] + ['--collapse-output-transforms', '1'] +
['--write-composite-transform'] +
['--winsorize-image-intensities', '[0.01,0.99]'] +
optional(conf.use_histogram_matching,
lambda _: ['--use-histogram-matching', '1']) +
['--float', '0'] + [
'--output',
'[' + os.path.join(xfm_source_to_target.dir,
xfm_source_to_target.filename_wo_ext) + ']'
] + ['--transform', conf.transformation_model] +
optional(initial_source_transform,
lambda xfm: ['--initial-fixed-transform', xfm.path]) +
optional(initial_target_transform,
lambda xfm: ['--initial-moving-transform', xfm.path]) +
flatten(*[['--metric', render_metric(m)]
for m in conf.metrics]) + mask_arr +
['--shrink-factors', 'x'.join(str(s) for s in conf.shrink_factors)] + [
'--smoothing-sigmas', 'x'.join(
str(s) for s in conf.smoothing_sigmas)
])
# shamelessly stolen from ANTS, probably inaccurate
# see comments re: mincblur memory configuration
def set_memory(st, mem_cfg):
# see comments re: mincblur memory configuration
voxels = reduce(mul, volumeFromFile(source.path).getSizes())
mem_per_voxel = (
mem_cfg.mem_per_voxel_coarse
if 0 in conf.convergence.iterations[-1:] #-2?
# yikes ... this parsing should be done earlier
else mem_cfg.mem_per_voxel_fine)
st.setMem(mem_cfg.base_mem + voxels * mem_per_voxel)
cmd.when_runnable_hooks.append(
lambda st: set_memory(st, mem_cfg=default_ANTSRegistration_mem_cfg))
s.add(cmd)
# create file names for the two output files. It's better to use our standard
# mincresample command for this, because that also deals with any associated
# masks, whereas antsRegistration would only resample the input files.
resampled_source = (s.defer(
mincresample(img=source,
xfm=xfm_source_to_target,
like=target,
interpolation=Interpolation.sinc,
subdir=resample_subdir)) if resample_source else None)
resampled_target = (s.defer(
mincresample(img=target,
xfm=xfm_target_to_source,
like=source,
interpolation=Interpolation.sinc,
subdir=resample_subdir)) if resample_target else None)
# return an XfmHandler for both the forward and the inverse transformations
return Result(stages=s,
output=XfmHandler(source=source,
target=target,
xfm=xfm_source_to_target,
resampled=resampled_source,
inverse=XfmHandler(
source=target,
target=source,
xfm=xfm_target_to_source,
resampled=resampled_target)))
