def NLIN_pipeline(options):
# if options.application.files is None:
# raise ValueError("Please, some files! (or try '--help')") # TODO make a util procedure for this
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
# TODO this is tedious and annoyingly similar to the registration chain and MBM and LSQ6 ...
processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
nlin_dir = os.path.join(output_dir, pipeline_name + "_nlin")
resolution = (options.registration.resolution # TODO does using the finest resolution here make sense?
or min([get_resolution_from_file(f) for f in options.application.files]))
imgs = get_imgs(options.application)
# imgs = [MincAtom(f, pipeline_sub_dir=processed_dir) for f in options.application.files]
# determine NLIN settings by overriding defaults with
# any settings present in protocol file, if it exists
# could add a hook to print a message announcing completion, output files,
# add more stages here to make a CSV
initial_target_mask = MincAtom(options.nlin.target_mask) if options.nlin.target_mask else None
initial_target = MincAtom(options.nlin.target, mask=initial_target_mask)
full_hierarchy = get_nonlinear_configuration_from_options(nlin_protocol=options.nlin.nlin_protocol,
reg_method=options.nlin.reg_method,
file_resolution=resolution)
s = Stages()
nlin_result = s.defer(nlin_build_model(imgs, initial_target=initial_target, conf=full_hierarchy, nlin_dir=nlin_dir))
# TODO return these?
inverted_xfms = [s.defer(invert_xfmhandler(xfm)) for xfm in nlin_result.output]
if options.stats.calc_stats:
# TODO: put the stats part behind a flag ...
determinants = [s.defer(determinants_at_fwhms(
xfm=inv_xfm,
inv_xfm=xfm,
blur_fwhms=options.stats.stats_kernels))
for xfm, inv_xfm in zip(nlin_result.output, inverted_xfms)]
return Result(stages=s,
output=Namespace(nlin_xfms=nlin_result,
avg_img=nlin_result.avg_img,
determinants=determinants))
else:
# there's no consistency in what gets returned, yikes ...
return Result(stages=s, output=Namespace(nlin_xfms=nlin_result, avg_img=nlin_result.avg_img))
def NLIN_pipeline(options):
if options.application.files is None:
raise ValueError("Please, some files! (or try '--help')") # TODO make a util procedure for this
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
# TODO this is tedious and annoyingly similar to the registration chain and MBM and LSQ6 ...
processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
nlin_dir = os.path.join(output_dir, pipeline_name + "_nlin")
resolution = (options.registration.resolution # TODO does using the finest resolution here make sense?
or min([get_resolution_from_file(f) for f in options.application.files]))
imgs = [MincAtom(f, pipeline_sub_dir=processed_dir) for f in options.application.files]
# determine NLIN settings by overriding defaults with
# any settings present in protocol file, if it exists
# could add a hook to print a message announcing completion, output files,
# add more stages here to make a CSV
initial_target_mask = MincAtom(options.nlin.target_mask) if options.nlin.target_mask else None
initial_target = MincAtom(options.nlin.target, mask=initial_target_mask)
full_hierarchy = get_nonlinear_configuration_from_options(nlin_protocol=options.nlin.nlin_protocol,
flag_nlin_protocol=next(iter(options.nlin.flags_.nlin_protocol)),
reg_method=options.nlin.reg_method,
file_resolution=resolution)
s = Stages()
nlin_result = s.defer(nlin_build_model(imgs, initial_target=initial_target, conf=full_hierarchy, nlin_dir=nlin_dir))
# TODO return these?
inverted_xfms = [s.defer(invert_xfmhandler(xfm)) for xfm in nlin_result.output]
if options.stats.calc_stats:
# TODO: put the stats part behind a flag ...
determinants = [s.defer(determinants_at_fwhms(
xfm=inv_xfm,
inv_xfm=xfm,
blur_fwhms=options.stats.stats_kernels))
for xfm, inv_xfm in zip(nlin_result.output, inverted_xfms)]
return Result(stages=s,
output=Namespace(nlin_xfms=nlin_result,
avg_img=nlin_result.avg_img,
determinants=determinants))
else:
# there's no consistency in what gets returned, yikes ...
return Result(stages=s, output=Namespace(nlin_xfms=nlin_result, avg_img=nlin_result.avg_img))
def tamarack(imgs: pd.DataFrame, options):
# columns of the input df: `img` : MincAtom, `timept` : number, ...
# columns of the pride of models : 'timept' : number, 'model' : MincAtom
s = Stages()
# TODO some assertions that the pride_of_models, if provided, is correct, and that this is intended target type
def group_options(options, timepoint):
options = copy.deepcopy(options)
if options.mbm.lsq6.target_type == TargetType.pride_of_models:
options = copy.deepcopy(options)
targets = get_closest_model_from_pride_of_models(
pride_of_models_dict=get_pride_of_models_mapping(
pride_csv=options.mbm.lsq6.target_file,
output_dir=options.application.output_directory,
pipeline_name=options.application.pipeline_name),
time_point=timepoint)
options.mbm.lsq6 = options.mbm.lsq6.replace(
target_type=TargetType.initial_model,
target_file=targets.registration_standard.path)
# resolution = (options.registration.resolution
# or get_resolution_from_file(targets.registration_standard.path))
# options.registration = options.registration.replace(resolution=resolution)
# FIXME use of registration_standard here is quite wrong ...
# part of the trouble is that mbm calls registration_targets itself,
# so we can't send this RegistrationTargets to `mbm` directly ...
# one option: add yet another optional arg to `mbm` ...
else:
targets = s.defer(
registration_targets(lsq6_conf=options.mbm.lsq6,
app_conf=options.application,
reg_conf=options.registration,
first_input_file=imgs.filename.iloc[0]))
resolution = (options.registration.resolution
or get_resolution_from_file(
targets.registration_standard.path))
# This must happen after calling registration_targets otherwise it will resample to options.registration.resolution
options.registration = options.registration.replace(
resolution=resolution)
return options
# build all first-level models:
first_level_results = (
imgs # TODO 'group' => 'timept' ?
.groupby('group', as_index=False
) # the usual annoying pattern to do an aggregate with access
.aggregate({'file': lambda files: list(files)}
) # to the groupby object's keys ... TODO: fix
.rename(columns={
'file': "files"
}).assign(options=lambda df: df.apply(
axis=1, func=lambda row: group_options(options, row.group))
).assign(build_model=lambda df: df.apply(
axis=1,
func=lambda row: s.defer(
mbm(imgs=row.files,
options=row.options,
prefix="%s" % row.group,
output_dir=os.path.join(
options.application.output_directory, options
.application.pipeline_name + "_first_level",
"%s_processed" % row.group))))
).sort_values(by='group'))
if all(
first_level_results.options.map(
lambda opts: opts.registration.resolution) ==
first_level_results.options.iloc[0].registration.resolution):
options.registration = options.registration.replace(
resolution=first_level_results.options.iloc[0].registration.
resolution)
else:
raise ValueError(
"some first-level models are run at different resolutions, possibly not what you want ..."
)
# construction of the overall inter-average transforms will be done iteratively (for efficiency/aesthetics),
# which doesn't really fit the DataFrame mold ...
full_hierarchy = get_nonlinear_configuration_from_options(
nlin_protocol=options.mbm.nlin.nlin_protocol,
reg_method=options.mbm.nlin.reg_method,
file_resolution=options.registration.resolution)
# FIXME no good can come of this
nlin_protocol = full_hierarchy.confs[-1] if isinstance(
full_hierarchy, MultilevelANTSConf) else full_hierarchy
# first register consecutive averages together:
average_registrations = (
first_level_results[:-1].assign(
next_model=list(first_level_results[1:].build_model))
# TODO: we should be able to do lsq6 registration here as well!
.assign(xfm=lambda df: df.apply(
axis=1,
func=lambda row: s.defer(
lsq12_nlin(source=row.build_model.avg_img,
target=row.next_model.avg_img,
lsq12_conf=get_linear_configuration_from_options(
options.mbm.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=options.registration.resolution
),
nlin_conf=nlin_protocol)))))
# now compose the above transforms to produce transforms from each average to the common average:
common_time_pt = options.tamarack.common_time_pt
common_model = first_level_results[
first_level_results.group ==
common_time_pt].iloc[0].build_model.avg_img
#common = average_registrations[average_registrations.group == common_time_pt].iloc[0]
before = average_registrations[
average_registrations.group <
common_time_pt] # asymmetry in before/after since
after = average_registrations[
average_registrations.group >=
common_time_pt] # we used `next_`, not `previous_`
# compose 1st and 2nd level transforms and resample into the common average space:
def suffixes(xs):
if len(xs) == 0:
return [[]]
else:
ys = suffixes(xs[1:])
return [[xs[0]] + ys[0]] + ys
def prefixes(xs):
if len(xs) == 0:
return [[]]
else:
ys = prefixes(xs[1:])
return ys + [ys[-1] + [xs[0]]]
xfms_to_common = (first_level_results.assign(
uncomposed_xfms=suffixes(list(before.xfm))[:-1] + [None] +
prefixes(list(after.xfm))[1:]).assign(
xfm_to_common=lambda df: df.apply(
axis=1,
func=lambda row: ((lambda x: s.defer(invert_xfmhandler(
x)) if row.group >= common_time_pt else x)(s.defer(
concat_xfmhandlers(
row.uncomposed_xfms,
name=("%s_to_common" if row.group < common_time_pt
else "%s_from_common") % row.group))))
if row.uncomposed_xfms is not None else None)).drop(
'uncomposed_xfms', axis=1)) # TODO None => identity??
# TODO indexing here is not good ...
first_level_determinants = pd.concat(list(
first_level_results.build_model.apply(
lambda x: x.determinants.assign(first_level_avg=x.avg_img))),
ignore_index=True)
resampled_determinants = (pd.merge(
left=first_level_determinants,
right=xfms_to_common.assign(source=lambda df: df.xfm_to_common.apply(
lambda x: x.source if x is not None else None)),
left_on="first_level_avg",
right_on='source').assign(
resampled_log_full_det=lambda df: df.apply(
axis=1,
func=lambda row: s.defer(
mincresample_new(img=row.log_full_det,
xfm=row.xfm_to_common.xfm,
like=common_model))
if row.xfm_to_common is not None else row.img),
resampled_log_nlin_det=lambda df: df.apply(
axis=1,
func=lambda row: s.defer(
mincresample_new(img=row.log_nlin_det,
xfm=row.xfm_to_common.xfm,
like=common_model))
if row.xfm_to_common is not None else row.img)))
inverted_overall_xfms = pd.Series({
xfm: (s.defer(concat_xfmhandlers([xfm, row.xfm_to_common]))
if row.xfm_to_common is not None else xfm)
for _ix, row in xfms_to_common.iterrows()
for xfm in row.build_model.xfms.lsq12_nlin_xfm
})
overall_xfms = inverted_overall_xfms.apply(
lambda x: s.defer(invert_xfmhandler(x)))
overall_determinants = determinants_at_fwhms(
xfms=overall_xfms,
blur_fwhms=options.mbm.stats.stats_kernels,
inv_xfms=inverted_overall_xfms)
# TODO turn off bootstrap as with two-level code?
# TODO combine into one data frame
return Result(stages=s,
output=Namespace(
first_level_results=first_level_results,
overall_determinants=overall_determinants,
resampled_determinants=resampled_determinants.drop(
['options'], axis=1)))
def tamarack(imgs : pd.DataFrame, options):
# columns of the input df: `img` : MincAtom, `timept` : number, ...
# columns of the pride of models : 'timept' : number, 'model' : MincAtom
s = Stages()
# TODO some assertions that the pride_of_models, if provided, is correct, and that this is intended target type
def group_options(options, timepoint):
options = copy.deepcopy(options)
if options.mbm.lsq6.target_type == TargetType.pride_of_models:
options = copy.deepcopy(options)
targets = get_closest_model_from_pride_of_models(pride_of_models_dict=get_pride_of_models_mapping(
pride_csv=options.mbm.lsq6.target_file,
output_dir=options.application.output_directory,
pipeline_name=options.application.pipeline_name),
time_point=timepoint)
options.mbm.lsq6 = options.mbm.lsq6.replace(target_type=TargetType.initial_model,
target_file=targets.registration_standard.path)
# resolution = (options.registration.resolution
# or get_resolution_from_file(targets.registration_standard.path))
# options.registration = options.registration.replace(resolution=resolution)
# FIXME use of registration_standard here is quite wrong ...
# part of the trouble is that mbm calls registration_targets itself,
# so we can't send this RegistrationTargets to `mbm` directly ...
# one option: add yet another optional arg to `mbm` ...
else:
targets = s.defer(registration_targets(lsq6_conf=options.mbm.lsq6,
app_conf=options.application, reg_conf=options.registration,
first_input_file=imgs.filename.iloc[0]))
resolution = (options.registration.resolution or
get_resolution_from_file(targets.registration_standard.path))
# This must happen after calling registration_targets otherwise it will resample to options.registration.resolution
options.registration = options.registration.replace(resolution=resolution)
return options
# build all first-level models:
first_level_results = (
imgs # TODO 'group' => 'timept' ?
.groupby('group', as_index=False) # the usual annoying pattern to do an aggregate with access
.aggregate({ 'file' : lambda files: list(files) }) # to the groupby object's keys ... TODO: fix
.rename(columns={ 'file' : "files" })
.assign(options=lambda df: df.apply(axis=1, func=lambda row: group_options(options, row.group)))
.assign(build_model=lambda df:
df.apply(axis=1,
func=lambda row: s.defer(
mbm(imgs=row.files,
options=row.options,
prefix="%s" % row.group,
output_dir=os.path.join(
options.application.output_directory,
options.application.pipeline_name + "_first_level",
"%s_processed" % row.group)))))
.sort_values(by='group')
)
if all(first_level_results.options.map(lambda opts: opts.registration.resolution)
== first_level_results.options.iloc[0].registration.resolution):
options.registration = options.registration.replace(
resolution=first_level_results.options.iloc[0].registration.resolution)
else:
raise ValueError("some first-level models are run at different resolutions, possibly not what you want ...")
# construction of the overall inter-average transforms will be done iteratively (for efficiency/aesthetics),
# which doesn't really fit the DataFrame mold ...
full_hierarchy = get_nonlinear_configuration_from_options(
nlin_protocol=options.mbm.nlin.nlin_protocol,
reg_method=options.mbm.nlin.reg_method,
file_resolution=options.registration.resolution)
# FIXME no good can come of this
nlin_protocol = full_hierarchy.confs[-1] if isinstance(full_hierarchy, MultilevelANTSConf) else full_hierarchy
# first register consecutive averages together:
average_registrations = (
first_level_results[:-1]
.assign(next_model=list(first_level_results[1:].build_model))
# TODO: we should be able to do lsq6 registration here as well!
.assign(xfm=lambda df: df.apply(axis=1, func=lambda row: s.defer(
lsq12_nlin(source=row.build_model.avg_img,
target=row.next_model.avg_img,
lsq12_conf=get_linear_configuration_from_options(
options.mbm.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=options.registration.resolution),
nlin_conf=nlin_protocol)))))
# now compose the above transforms to produce transforms from each average to the common average:
common_time_pt = options.tamarack.common_time_pt
common_model = first_level_results[first_level_results.group == common_time_pt].iloc[0].build_model.avg_img
#common = average_registrations[average_registrations.group == common_time_pt].iloc[0]
before = average_registrations[average_registrations.group < common_time_pt] # asymmetry in before/after since
after = average_registrations[average_registrations.group >= common_time_pt] # we used `next_`, not `previous_`
# compose 1st and 2nd level transforms and resample into the common average space:
def suffixes(xs):
if len(xs) == 0:
return [[]]
else:
ys = suffixes(xs[1:])
return [[xs[0]] + ys[0]] + ys
def prefixes(xs):
if len(xs) == 0:
return [[]]
else:
ys = prefixes(xs[1:])
return ys + [ys[-1] + [xs[0]]]
xfms_to_common = (
first_level_results
.assign(uncomposed_xfms=suffixes(list(before.xfm))[:-1] + [None] + prefixes(list(after.xfm))[1:])
.assign(xfm_to_common=lambda df: df.apply(axis=1, func=lambda row:
((lambda x: s.defer(invert_xfmhandler(x)) if row.group >= common_time_pt else x)
(s.defer(concat_xfmhandlers(row.uncomposed_xfms,
name=("%s_to_common"
if row.group < common_time_pt
else "%s_from_common") % row.group))))
if row.uncomposed_xfms is not None else None))
.drop('uncomposed_xfms', axis=1)) # TODO None => identity??
# TODO indexing here is not good ...
first_level_determinants = pd.concat(list(first_level_results.build_model.apply(
lambda x: x.determinants.assign(first_level_avg=x.avg_img))),
ignore_index=True)
resampled_determinants = (
pd.merge(left=first_level_determinants,
right=xfms_to_common.assign(source=lambda df: df.xfm_to_common.apply(
lambda x:
x.source if x is not None else None)),
left_on="first_level_avg", right_on='source')
.assign(resampled_log_full_det=lambda df: df.apply(axis=1, func=lambda row:
s.defer(mincresample_new(img=row.log_full_det,
xfm=row.xfm_to_common.xfm,
like=common_model))
if row.xfm_to_common is not None else row.img),
resampled_log_nlin_det=lambda df: df.apply(axis=1, func=lambda row:
s.defer(mincresample_new(img=row.log_nlin_det,
xfm=row.xfm_to_common.xfm,
like=common_model))
if row.xfm_to_common is not None else row.img))
)
inverted_overall_xfms = pd.Series({ xfm : (s.defer(concat_xfmhandlers([xfm, row.xfm_to_common]))
if row.xfm_to_common is not None else xfm)
for _ix, row in xfms_to_common.iterrows()
for xfm in row.build_model.xfms.lsq12_nlin_xfm })
overall_xfms = inverted_overall_xfms.apply(lambda x: s.defer(invert_xfmhandler(x)))
overall_determinants = determinants_at_fwhms(xfms=overall_xfms,
blur_fwhms=options.mbm.stats.stats_kernels,
inv_xfms=inverted_overall_xfms)
# TODO turn off bootstrap as with two-level code?
# TODO combine into one data frame
return Result(stages=s, output=Namespace(first_level_results=first_level_results,
overall_determinants=overall_determinants,
resampled_determinants=resampled_determinants.drop(
['options'],
axis=1)))
def mbm(imgs : List[MincAtom], options : MBMConf, prefix : str, output_dir : str = ""):
# TODO could also allow pluggable pipeline parts e.g. LSQ6 could be substituted out for the modified LSQ6
# for the kidney tips, etc...
# TODO this is tedious and annoyingly similar to the registration chain ...
lsq6_dir = os.path.join(output_dir, prefix + "_lsq6")
lsq12_dir = os.path.join(output_dir, prefix + "_lsq12")
nlin_dir = os.path.join(output_dir, prefix + "_nlin")
s = Stages()
if len(imgs) == 0:
raise ValueError("Please, some files!")
# FIXME: why do we have to call registration_targets *outside* of lsq6_nuc_inorm? is it just because of the extra
# options required? Also, shouldn't options.registration be a required input (as it contains `input_space`) ...?
targets = registration_targets(lsq6_conf=options.mbm.lsq6,
app_conf=options.application,
first_input_file=imgs[0].path)
# TODO this is quite tedious and duplicates stuff in the registration chain ...
resolution = (options.registration.resolution or
get_resolution_from_file(targets.registration_standard.path))
options.registration = options.registration.replace(resolution=resolution)
# FIXME it probably makes most sense if the lsq6 module itself (even within lsq6_nuc_inorm) handles the run_lsq6
# setting (via use of the identity transform) since then this doesn't have to be implemented for every pipeline
if options.mbm.lsq6.run_lsq6:
lsq6_result = s.defer(lsq6_nuc_inorm(imgs=imgs,
resolution=resolution,
registration_targets=targets,
lsq6_dir=lsq6_dir,
lsq6_options=options.mbm.lsq6))
else:
# TODO don't actually do this resampling if not required (i.e., if the imgs already have the same grids)
identity_xfm = s.defer(param2xfm(out_xfm=FileAtom(name="identity.xfm")))
lsq6_result = [XfmHandler(source=img, target=img, xfm=identity_xfm,
resampled=s.defer(mincresample_new(img=img,
like=targets.registration_standard,
xfm=identity_xfm)))
for img in imgs]
# what about running nuc/inorm without a linear registration step??
full_hierarchy = get_nonlinear_configuration_from_options(nlin_protocol=options.mbm.nlin.nlin_protocol,
reg_method=options.mbm.nlin.reg_method,
file_resolution=resolution)
lsq12_nlin_result = s.defer(lsq12_nlin_build_model(imgs=[xfm.resampled for xfm in lsq6_result],
resolution=resolution,
lsq12_dir=lsq12_dir,
nlin_dir=nlin_dir,
nlin_prefix=prefix,
lsq12_conf=options.mbm.lsq12,
nlin_conf=full_hierarchy))
inverted_xfms = [s.defer(invert_xfmhandler(xfm)) for xfm in lsq12_nlin_result.output]
determinants = s.defer(determinants_at_fwhms(
xfms=inverted_xfms,
inv_xfms=lsq12_nlin_result.output,
blur_fwhms=options.mbm.stats.stats_kernels))
overall_xfms = [s.defer(concat_xfmhandlers([rigid_xfm, lsq12_nlin_xfm]))
for rigid_xfm, lsq12_nlin_xfm in zip(lsq6_result, lsq12_nlin_result.output)]
output_xfms = (pd.DataFrame({ "rigid_xfm" : lsq6_result, # maybe don't return this if LSQ6 not run??
"lsq12_nlin_xfm" : lsq12_nlin_result.output,
"overall_xfm" : overall_xfms }))
# we could `merge` the determinants with this table, but preserving information would cause lots of duplication
# of the transforms (or storing determinants in more columns, but iterating over dynamically known columns
# seems a bit odd ...)
# TODO transpose these fields?})
#avg_img=lsq12_nlin_result.avg_img, # inconsistent w/ WithAvgImgs[...]-style outputs
# "determinants" : determinants })
#output.avg_img = lsq12_nlin_result.avg_img
#output.determinants = determinants # TODO temporary - remove once incorporated properly into `output` proper
# TODO add more of lsq12_nlin_result?
# FIXME: this needs to go outside of the `mbm` function to avoid being run from within other pipelines (or
# those other pipelines need to turn off this option)
# TODO return some MAGeT stuff from MBM function ??
# if options.mbm.mbm.run_maget:
# import copy
# maget_options = copy.deepcopy(options) #Namespace(maget=options)
# #maget_options
# #maget_options.maget = maget_options.mbm
# #maget_options.execution = options.execution
# #maget_options.application = options.application
# maget_options.maget = options.mbm.maget
# del maget_options.mbm
#
# s.defer(maget([xfm.resampled for xfm in lsq6_result],
# options=maget_options,
# prefix="%s_MAGeT" % prefix,
# output_dir=os.path.join(output_dir, prefix + "_processed")))
# should also move outside `mbm` function ...
#if options.mbm.thickness.run_thickness:
# if not options.mbm.segmentation.run_maget:
# warnings.warn("MAGeT files (atlases, protocols) are needed to run thickness calculation.")
# # run MAGeT to segment the nlin average:
# import copy
# maget_options = copy.deepcopy(options) #Namespace(maget=options)
# maget_options.maget = options.mbm.maget
# del maget_options.mbm
# segmented_avg = s.defer(maget(imgs=[lsq12_nlin_result.avg_img],
# options=maget_options,
# output_dir=os.path.join(options.application.output_directory,
# prefix + "_processed"),
# prefix="%s_thickness_MAGeT" % prefix)).ix[0].img
# thickness = s.defer(cortical_thickness(xfms=pd.Series(inverted_xfms), atlas=segmented_avg,
# label_mapping=FileAtom(options.mbm.thickness.label_mapping),
# atlas_fwhm=0.56, thickness_fwhm=0.56)) # TODO magic fwhms
# # TODO write CSV -- should `cortical_thickness` do this/return a table?
# FIXME: this needs to go outside of the `mbm` function to avoid being run from within other pipelines (or
# those other pipelines need to turn off this option)
if options.mbm.common_space.do_common_space_registration:
warnings.warn("This feature is experimental ...")
if not options.mbm.common_space.common_space_model:
raise ValueError("No common space template provided!")
# TODO allow lsq6 registration as well ...
common_space_model = MincAtom(options.mbm.common_space.common_space_model,
pipeline_sub_dir=os.path.join(options.application.output_directory,
options.application.pipeline_name + "_processed"))
# TODO allow different lsq12/nlin config params than the ones used in MBM ...
# WEIRD ... see comment in lsq12_nlin code ...
nlin_conf = full_hierarchy.confs[-1] if isinstance(full_hierarchy, MultilevelMincANTSConf) else full_hierarchy
# also weird that we need to call get_linear_configuration_from_options here ... ?
lsq12_conf = get_linear_configuration_from_options(conf=options.mbm.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=resolution)
xfm_to_common = s.defer(lsq12_nlin(source=lsq12_nlin_result.avg_img, target=common_space_model,
lsq12_conf=lsq12_conf, nlin_conf=nlin_conf,
resample_source=True))
model_common = s.defer(mincresample_new(img=lsq12_nlin_result.avg_img,
xfm=xfm_to_common.xfm, like=common_space_model,
postfix="_common"))
overall_xfms_common = [s.defer(concat_xfmhandlers([rigid_xfm, nlin_xfm, xfm_to_common]))
for rigid_xfm, nlin_xfm in zip(lsq6_result, lsq12_nlin_result.output)]
xfms_common = [s.defer(concat_xfmhandlers([nlin_xfm, xfm_to_common]))
for nlin_xfm in lsq12_nlin_result.output]
output_xfms = output_xfms.assign(xfm_common=xfms_common, overall_xfm_common=overall_xfms_common)
log_nlin_det_common, log_full_det_common = [dets.map(lambda d:
s.defer(mincresample_new(
img=d,
xfm=xfm_to_common.xfm,
like=common_space_model,
postfix="_common",
extra_flags=("-keep_real_range",),
interpolation=Interpolation.nearest_neighbour)))
for dets in (determinants.log_nlin_det, determinants.log_full_det)]
determinants = determinants.assign(log_nlin_det_common=log_nlin_det_common,
log_full_det_common=log_full_det_common)
output = Namespace(avg_img=lsq12_nlin_result.avg_img, xfms=output_xfms, determinants=determinants)
if options.mbm.common_space.do_common_space_registration:
output.model_common = model_common
return Result(stages=s, output=output)
def maget_mask(imgs : List[MincAtom], atlases, options):
s = Stages()
resample = np.vectorize(mincresample_new, excluded={"extra_flags"})
defer = np.vectorize(s.defer)
lsq12_conf = get_linear_configuration_from_options(options.maget.lsq12,
LinearTransType.lsq12,
options.registration.resolution)
masking_nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.maget.masking_nlin_protocol,
options.maget.maget.mask_method,
options.registration.resolution)
masking_alignments = pd.DataFrame({ 'img' : img,
'atlas' : atlas,
'xfm' : s.defer(lsq12_nlin(source=img, target=atlas,
lsq12_conf=lsq12_conf,
nlin_conf=masking_nlin_hierarchy,
resample_source=False))}
for img in imgs for atlas in atlases)
# propagate a mask to each image using the above `alignments` as follows:
# - for each image, voxel_vote on the masks propagated to that image to get a suitable mask
# - run mincmath -clobber -mult <img> <voted_mask> to apply the mask to the files
masked_img = (
masking_alignments
.assign(resampled_mask=lambda df: defer(resample(img=df.atlas.apply(lambda x: x.mask),
xfm=df.xfm.apply(lambda x: x.xfm),
like=df.img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
postfix="-input-mask",
subdir="tmp",
# TODO annoying hack; fix mincresample(_mask) ...:
#new_name_wo_ext=df.apply(lambda row:
# "%s_to_%s-input-mask" % (row.atlas.filename_wo_ext,
# row.img.filename_wo_ext),
# axis=1),
extra_flags=("-keep_real_range",))))
.groupby('img', sort=False, as_index=False)
# sort=False: just for speed (might also need to implement more comparison methods on `MincAtom`s)
.aggregate({'resampled_mask' : lambda masks: list(masks)})
.rename(columns={"resampled_mask" : "resampled_masks"})
.assign(voted_mask=lambda df: df.apply(axis=1,
func=lambda row:
s.defer(voxel_vote(label_files=row.resampled_masks,
name="%s_voted_mask" % row.img.filename_wo_ext,
output_dir=os.path.join(row.img.output_sub_dir,
"tmp")))))
.assign(masked_img=lambda df:
df.apply(axis=1,
func=lambda row:
s.defer(mincmath(op="mult",
# img must precede mask here
# for output image range to be correct:
vols=[row.img, row.voted_mask],
new_name="%s_masked" % row.img.filename_wo_ext,
subdir="resampled"))))) #['img']
# resample the atlas images back to the input images:
# (note: this doesn't modify `masking_alignments`, but only stages additional outputs)
masking_alignments.assign(resampled_img=lambda df:
defer(resample(img=df.atlas,
xfm=df.xfm.apply(lambda x: x.xfm),
subdir="tmp",
# TODO delete this stupid hack:
#new_name_wo_ext=df.apply(lambda row:
# "%s_to_%s-resampled" % (row.atlas.filename_wo_ext,
# row.img.filename_wo_ext),
# axis=1),
like=df.img, invert=True)))
# replace the table of alignments with a new one with masked images
masking_alignments = (pd.merge(left=masking_alignments.assign(unmasked_img=lambda df: df.img),
right=masked_img,
on=["img"], how="right", sort=False)
.assign(img=lambda df: df.masked_img))
return Result(stages=s, output=masking_alignments)
def maget(imgs : List[MincAtom], options, prefix, output_dir): # FIXME prefix, output_dir aren't used !!
s = Stages()
maget_options = options.maget.maget
pipeline_sub_dir = os.path.join(options.application.output_directory,
options.application.pipeline_name + "_atlases")
if maget_options.atlas_lib is None:
raise ValueError("Need some atlases ...")
#atlas_dir = os.path.join(output_dir, "input_atlases") ???
# TODO should alternately accept a CSV file ...
atlas_library = read_atlas_dir(atlas_lib=maget_options.atlas_lib, pipeline_sub_dir=pipeline_sub_dir)
if len(atlas_library) == 0:
raise ValueError("No atlases found in specified directory '%s' ..." % options.maget.maget.atlas_lib)
num_atlases_needed = min(maget_options.max_templates, len(atlas_library))
# TODO arbitrary; could choose atlases better ...
atlases = atlas_library[:num_atlases_needed]
# TODO issue a warning if not all atlases used or if more atlases requested than available?
# TODO also, doesn't slicing with a higher number (i.e., if max_templates > n) go to the end of the list anyway?
lsq12_conf = get_linear_configuration_from_options(options.maget.lsq12,
LinearTransType.lsq12,
options.registration.resolution)
masking_nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.maget.masking_nlin_protocol,
options.maget.maget.mask_method,
options.registration.resolution)
nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.nlin.nlin_protocol,
options.maget.nlin.reg_method,
options.registration.resolution)
resample = np.vectorize(mincresample_new, excluded={"extra_flags"})
defer = np.vectorize(s.defer)
# plan the basic registrations between all image-atlas pairs; store the result paths in a table
masking_alignments = pd.DataFrame({ 'img' : img,
'atlas' : atlas,
'xfm' : s.defer(lsq12_nlin(source=img, target=atlas,
lsq12_conf=lsq12_conf,
nlin_conf=masking_nlin_hierarchy,
resample_source=False))}
for img in imgs for atlas in atlases)
if maget_options.mask or maget_options.mask_only:
masking_alignments = s.defer(maget_mask(imgs, atlases, options))
masked_atlases = atlases.apply(lambda atlas:
s.defer(mincmath(op='mult', vols=[atlas, atlas.mask], subdir="resampled",
new_name="%s_masked" % atlas.filename_wo_ext)))
# now propagate only the masked form of the images and atlases:
imgs = masking_alignments.img
atlases = masked_atlases # TODO is this needed?
if maget_options.mask_only:
# register each input to each atlas, creating a mask
return Result(stages=s, output=masking_alignments) # TODO rename `alignments` to `registrations`??
else:
del masking_alignments
# this `del` is just to verify that we don't accidentally use this later, since my intent is that these
# coarser alignments shouldn't be re-used, just the masked images they create; can be removed later
# if a sensible use is found
if maget_options.pairwise:
def choose_new_templates(ts, n):
# currently silly, but we might implement a smarter method ...
# FIXME what if there aren't enough other imgs around?! This silently goes weird ...
return ts[:n+1] # n+1 instead of n: choose one more since we won't use image as its own template ...
new_templates = choose_new_templates(ts=imgs, n=maget_options.max_templates)
# note these images are the masked ones if masking was done ...
# TODO write a function to do these alignments and the image->atlas one above
# align the new templates chosen from the images to the initial atlases:
new_template_to_atlas_alignments = (
pd.DataFrame({ 'img' : template,
'atlas' : atlas,
'xfm' : s.defer(lsq12_nlin(source=template, target=atlas,
lsq12_conf=lsq12_conf,
nlin_conf=nlin_hierarchy,
resample_source=False))}
for template in new_templates for atlas in atlases))
# ... and these atlases are multiplied by their masks (but is this necessary?)
# label the new templates from resampling the atlas labels onto them:
# TODO now vote on the labels to be used for the new templates ...
# TODO extract into procedure?
new_templates_labelled = (
new_template_to_atlas_alignments
.assign(resampled_labels=lambda df: defer(
resample(img=df.atlas.apply(lambda x: x.labels),
xfm=df.xfm.apply(lambda x: x.xfm),
interpolation=Interpolation.nearest_neighbour,
extra_flags=("-keep_real_range",),
like=df.img, invert=True)))
.groupby('img', sort=False, as_index=False)
.aggregate({'resampled_labels' : lambda labels: list(labels)})
.assign(voted_labels=lambda df: df.apply(axis=1,
func=lambda row:
s.defer(voxel_vote(label_files=row.resampled_labels,
name="%s_template_labels" %
row.img.filename_wo_ext,
output_dir=os.path.join(
row.img.pipeline_sub_dir,
row.img.output_sub_dir,
"labels"))))))
# TODO write a procedure for this assign-groupby-aggregate-rename...
# FIXME should be in above algebraic manipulation but MincAtoms don't support flexible immutable updating
for row in pd.merge(left=new_template_to_atlas_alignments, right=new_templates_labelled,
on=["img"], how="right", sort=False).itertuples():
row.img.labels = s.defer(mincresample_new(img=row.voted_labels, xfm=row.xfm.xfm, like=row.img,
invert=True, interpolation=Interpolation.nearest_neighbour,
#postfix="-input-labels",
# this makes names really long ...:
# TODO this doesn't work for running MAGeT on the nlin avg:
#new_name_wo_ext="%s_on_%s" %
# (row.voted_labels.filename_wo_ext,
# row.img.filename_wo_ext),
#postfix="_labels_via_%s" % row.xfm.xfm.filename_wo_ext,
new_name_wo_ext="%s_via_%s" % (row.voted_labels.filename_wo_ext,
row.xfm.xfm.filename_wo_ext),
extra_flags=("-keep_real_range",)))
# now that the new templates have been labelled, combine with the atlases:
# FIXME use the masked atlases created earlier ??
all_templates = pd.concat([new_templates_labelled.img, atlases], ignore_index=True)
# now take union of the resampled labels from the new templates with labels from the original atlases:
#all_alignments = pd.concat([image_to_template_alignments,
# alignments.rename(columns={ "atlas" : "template" })],
# ignore_index=True, join="inner")
else:
all_templates = atlases
# now register each input to each selected template
# N.B.: Even though we've already registered each image to each initial atlas, this happens again here,
# but using `nlin_hierarchy` instead of `masking_nlin_hierarchy` as options.
# This is not 'work-efficient' in the sense that this computation happens twice (although
# hopefully at greater precision the second time!), but the idea is to run a coarse initial
# registration to get a mask and then do a better registration with that mask (though I'm not
# sure exactly when this is faster than doing a single registration).
# This _can_ allow the overall computation to finish more rapidly
# (depending on the relative speed of the two alignment methods/parameters,
# number of atlases and other templates used, number of cores available, etc.).
image_to_template_alignments = (
pd.DataFrame({ "img" : img,
"template" : template_img,
"xfm" : xfm }
for img in imgs # TODO use the masked imgs here?
for template_img in
all_templates
# FIXME delete this one alignment
#labelled_templates[labelled_templates.img != img]
# since equality is equality of filepaths (a bit dangerous)
# TODO is there a more direct/faster way just to delete the template?
for xfm in [s.defer(lsq12_nlin(source=img, target=template_img,
lsq12_conf=lsq12_conf,
nlin_conf=nlin_hierarchy))]
)
)
# now do a voxel_vote on all resampled template labels, just as earlier with the masks
voted = (image_to_template_alignments
.assign(resampled_labels=lambda df:
defer(resample(img=df.template.apply(lambda x: x.labels),
# FIXME bug: at this point templates from template_alignments
# don't have associated labels (i.e., `None`s) -- fatal
xfm=df.xfm.apply(lambda x: x.xfm),
interpolation=Interpolation.nearest_neighbour,
extra_flags=("-keep_real_range",),
like=df.img, invert=True)))
.groupby('img', sort=False)
# TODO the pattern groupby-aggregate(lambda x: list(x))-reset_index-assign is basically a hack
# to do a groupby-assign with access to the group name;
# see http://stackoverflow.com/a/30224447/849272 for a better solution
# (note this pattern occurs several times in MAGeT and two-level code)
.aggregate({'resampled_labels' : lambda labels: list(labels)})
.reset_index()
.assign(voted_labels=lambda df: defer(np.vectorize(voxel_vote)(label_files=df.resampled_labels,
output_dir=df.img.apply(
lambda x: os.path.join(
x.pipeline_sub_dir,
x.output_sub_dir))))))
# TODO doing mincresample -invert separately for the img->atlas xfm for mask, labels is silly
# (when Pydpiper's `mincresample` does both automatically)?
# blargh, another destructive update ...
for row in voted.itertuples():
row.img.labels = row.voted_labels
# returning voted_labels as a column is slightly redundant, but possibly useful ...
return Result(stages=s, output=voted) # voted.drop("voted_labels", axis=1))
def chain(options):
"""Create a registration chain pipeline from the given options."""
# TODO:
# one overall question for this entire piece of code is how
# we are going to make sure that we can concatenate/add all
# the transformations together. Many of the sub-registrations
# that are performed (inter-subject registration, lsq6 using
# multiple initial models) are applied to subsets of the entire
# data, making it harder to keep the mapping simple/straightforward
chain_opts = options.chain # type : ChainConf
s = Stages()
with open(options.chain.csv_file, 'r') as f:
subject_info = parse_csv(rows=f, common_time_pt=options.chain.common_time_point)
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
pipeline_processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
pipeline_lsq12_common_dir = os.path.join(output_dir, pipeline_name + "_lsq12_" + options.chain.common_time_point_name)
pipeline_nlin_common_dir = os.path.join(output_dir, pipeline_name + "_nlin_" + options.chain.common_time_point_name)
pipeline_montage_dir = os.path.join(output_dir, pipeline_name + "_montage")
pipeline_subject_info = map_over_time_pt_dict_in_Subject(
lambda subj_str: MincAtom(name=subj_str, pipeline_sub_dir=pipeline_processed_dir),
subject_info) # type: Dict[str, Subject[MincAtom]]
# verify that in input files are proper MINC files, and that there
# are no duplicates in the filenames
all_Minc_atoms = [] # type: List[MincAtom]
for s_id, subj in pipeline_subject_info.items():
for subj_time_pt, subj_filename in subj.time_pt_dict.items():
all_Minc_atoms.append(subj_filename)
# check_MINC_input_files takes strings, so pass along those instead of the actual MincAtoms
check_MINC_input_files([minc_atom.path for minc_atom in all_Minc_atoms])
if options.registration.input_space == InputSpace.lsq6 or \
options.registration.input_space == InputSpace.lsq12:
# the input files are not going through the lsq6 alignment. This is the place
# where they will all be resampled using a single like file, and get the same
# image dimensions/lengths/resolution. So in order for the subsequent stages to
# finish (mincaverage stages for instance), all files need to have the same
# image parameters:
check_MINC_files_have_equal_dimensions_and_resolution([minc_atom.path for minc_atom in all_Minc_atoms],
additional_msg="Given that the input images are "
"already in " + str(options.registration.input_space) +
" space, all input files need to have "
"the same dimensions/starts/step sizes.")
if options.registration.input_space not in InputSpace.__members__.values():
raise ValueError('unrecognized input space: %s; choices: %s' %
(options.registration.input_space, ','.join(InputSpace.__members__)))
if options.registration.input_space == InputSpace.native:
if options.lsq6.target_type == TargetType.bootstrap:
raise ValueError("\nA bootstrap model is ill-defined for the registration chain. "
"(Which file is the 'first' input file?). Please use the --lsq6-target "
"flag to specify a target for the lsq6 stage, or use an initial model.")
if options.lsq6.target_type == TargetType.pride_of_models:
pride_of_models_dict = get_pride_of_models_mapping(pride_csv=options.lsq6.target_file,
output_dir=options.application.output_directory,
pipeline_name=options.application.pipeline_name)
subj_id_to_subj_with_lsq6_xfm_dict = map_with_index_over_time_pt_dict_in_Subject(
lambda subj_atom, time_point:
s.defer(lsq6_nuc_inorm([subj_atom],
registration_targets=get_closest_model_from_pride_of_models(
pride_of_models_dict, time_point),
resolution=options.registration.resolution,
lsq6_options=options.lsq6,
lsq6_dir=None, # never used since no average
# (could call this "average_dir" with None -> no avg ?)
subject_matter=options.registration.subject_matter,
create_qc_images=False,
create_average=False))[0],
pipeline_subject_info) # type: Dict[str, Subject[XfmHandler]]
else:
# if we are not dealing with a pride of models, we can retrieve a fixed
# registration target for all input files:
targets = registration_targets(lsq6_conf=options.lsq6,
app_conf=options.application)
# we want to store the xfm handlers in the same shape as pipeline_subject_info,
# as such we will call lsq6_nuc_inorm for each file individually and simply extract
# the first (and only) element from the resulting list via s.defer(...)[0].
subj_id_to_subj_with_lsq6_xfm_dict = map_over_time_pt_dict_in_Subject(
lambda subj_atom:
s.defer(lsq6_nuc_inorm([subj_atom],
registration_targets=targets,
resolution=options.registration.resolution,
lsq6_options=options.lsq6,
lsq6_dir=None, # no average will be create, is just one file...
create_qc_images=False,
create_average=False,
subject_matter=options.registration.subject_matter)
)[0],
pipeline_subject_info) # type: Dict[str, Subject[XfmHandler]]
# create verification images to show the 6 parameter alignment
montageLSQ6 = pipeline_montage_dir + "/quality_control_montage_lsq6.png"
# TODO, base scaling factor on resolution of initial model or target
filesToCreateImagesFrom = []
for subj_id, subj in subj_id_to_subj_with_lsq6_xfm_dict.items():
for time_pt, subj_time_pt_xfm in subj.time_pt_dict.items():
filesToCreateImagesFrom.append(subj_time_pt_xfm.resampled)
# TODO it's strange that create_quality_control_images gets the montage directory twice
# TODO (in montages=output=montageLSQ6 and in montage_dir), suggesting a weirdness in create_q_c_images
lsq6VerificationImages = s.defer(create_quality_control_images(filesToCreateImagesFrom,
montage_output=montageLSQ6,
montage_dir=pipeline_montage_dir,
message=" the input images after the lsq6 alignment"))
# NB currently LSQ6 expects an array of files, but we have a map.
# possibilities:
# - note that pairwise is enough (except for efficiency -- redundant blurring, etc.)
# and just use the map fn above with an LSQ6 fn taking only a single source
# - rewrite LSQ6 to use such a (nested) map
# - write conversion which creates a tagged array from the map, performs LSQ6,
# and converts back
# - write 'over' which takes a registration, a data structure, and 'get/set' fns ...?
# Intersubject registration: LSQ12/NLIN registration of common-timepoint images
# The assumption here is that all these files are roughly aligned. Here is a toy
# schematic of what happens. In this example, the common timepoint is set timepoint 2:
#
# ------------
# subject A A_time_1 -> | A_time_2 | -> A_time_3
# subject B B_time_1 -> | B_time_2 | -> B_time_3
# subject C C_time_1 -> | C_time_2 | -> C_time_3
# ------------
# |
# group_wise registration on time point 2
#
# dictionary that holds the transformations from the intersubject images
# to the final common space average
intersubj_img_to_xfm_to_common_avg_dict = {} # type: Dict[MincAtom, XfmHandler]
if options.registration.input_space in (InputSpace.lsq6, InputSpace.lsq12):
# no registrations have been performed yet, so we can point to the input files
s_id_to_intersubj_img_dict = { s_id : subj.intersubject_registration_image
for s_id, subj in pipeline_subject_info.items() }
else:
# lsq6 aligned images
# When we ran the lsq6 alignment, we stored the XfmHandlers in the Subject dictionary. So when we call
# xfmhandler.intersubject_registration_image, this returns an XfmHandler. From which
# we want to extract the resampled file (in order to continue the registration with)
s_id_to_intersubj_img_dict = { s_id : subj_with_xfmhandler.intersubject_registration_image.resampled
for s_id, subj_with_xfmhandler in subj_id_to_subj_with_lsq6_xfm_dict.items() }
if options.application.verbose:
print("\nImages that are used for the inter-subject registration:")
print("ID\timage")
for subject in s_id_to_intersubj_img_dict:
print(subject + '\t' + s_id_to_intersubj_img_dict[subject].path)
# determine what configuration to use for the non linear registration
nonlinear_configuration = get_nonlinear_configuration_from_options(options.nlin.nlin_protocol,
options.nlin.reg_method,
options.registration.resolution)
if options.registration.input_space in [InputSpace.lsq6, InputSpace.native]:
intersubj_xfms = s.defer(lsq12_nlin_build_model(imgs=list(s_id_to_intersubj_img_dict.values()),
lsq12_conf=options.lsq12,
nlin_conf=nonlinear_configuration,
resolution=options.registration.resolution,
lsq12_dir=pipeline_lsq12_common_dir,
nlin_dir=pipeline_nlin_common_dir,
nlin_prefix="common"))
#, like={atlas_from_init_model_at_this_tp}
elif options.registration.input_space == InputSpace.lsq12:
#TODO: write reader that creates a mincANTS configuration out of an input protocol
# if we're starting with files that are already aligned with an affine transformation
# (overall scaling is also dealt with), then the target for the non linear registration
# should be the averge of the current input files.
first_nlin_target = s.defer(mincaverage(imgs=list(s_id_to_intersubj_img_dict.values()),
name_wo_ext="avg_of_input_files",
output_dir=pipeline_nlin_common_dir))
intersubj_xfms = s.defer(mincANTS_NLIN_build_model(imgs=list(s_id_to_intersubj_img_dict.values()),
initial_target=first_nlin_target,
nlin_dir=pipeline_nlin_common_dir,
conf=nonlinear_configuration))
intersubj_img_to_xfm_to_common_avg_dict = { xfm.source : xfm for xfm in intersubj_xfms.output }
# create one more convenience data structure: a mapping from subject_ID to the xfm_handler
# that contains the transformation from the subject at the common time point to the
# common time point average.
subj_ID_to_xfm_handler_to_common_avg = {}
for s_id, subj_at_common_tp in s_id_to_intersubj_img_dict.items():
subj_ID_to_xfm_handler_to_common_avg[s_id] = intersubj_img_to_xfm_to_common_avg_dict[subj_at_common_tp]
# create verification images to show the inter-subject alignment
montage_inter_subject = pipeline_montage_dir + "/quality_control_montage_inter_subject_registration.png"
avg_and_inter_subject_images = []
avg_and_inter_subject_images.append(intersubj_xfms.avg_img)
for xfmh in intersubj_xfms.output:
avg_and_inter_subject_images.append(xfmh.resampled)
inter_subject_verification_images = s.defer(create_quality_control_images(
imgs=avg_and_inter_subject_images,
montage_output=montage_inter_subject,
montage_dir=pipeline_montage_dir,
message=" the result of the inter-subject alignment"))
if options.application.verbose:
print("\nTransformations for intersubject images to final nlin common space:")
print("MincAtom\ttransformation")
for subj_atom, xfm_handler in intersubj_img_to_xfm_to_common_avg_dict.items():
print(subj_atom.path + '\t' + xfm_handler.xfm.path)
## within-subject registration
# In the toy scenario below:
# subject A A_time_1 -> A_time_2 -> A_time_3
# subject B B_time_1 -> B_time_2 -> B_time_3
# subject C C_time_1 -> C_time_2 -> C_time_3
#
# The following registrations are run:
# 1) A_time_1 -> A_time_2
# 2) A_time_2 -> A_time_3
#
# 3) B_time_1 -> B_time_2
# 4) B_time_2 -> B_time_3
#
# 5) C_time_1 -> C_time_2
# 6) C_time_2 -> C_time_3
subj_id_to_Subjec_for_within_dict = pipeline_subject_info
if options.registration.input_space == InputSpace.native:
# we started with input images that were not aligned whatsoever
# in this case we should use the images that were rigidly
# aligned files to continue the within-subject registration with
# # type: Dict[str, Subject[XfmHandler]]
subj_id_to_Subjec_for_within_dict = map_over_time_pt_dict_in_Subject(lambda x: x.resampled,
subj_id_to_subj_with_lsq6_xfm_dict)
if options.application.verbose:
print("\n\nWithin subject registrations:")
for s_id, subj in subj_id_to_Subjec_for_within_dict.items():
print("ID: ", s_id)
for time_pt, subj_img in subj.time_pt_dict.items():
print(time_pt, " ", subj_img.path)
print("\n")
# dictionary that maps subject IDs to a list containing:
# ( [(time_pt_n, time_pt_n+1, XfmHandler_from_n_to_n+1), ..., (,,,)],
# index_of_common_time_pt)
chain_xfms = { s_id : s.defer(intrasubject_registrations(
subj=subj,
linear_conf=default_lsq12_multilevel_minctracc,
nlin_conf=mincANTS_default_conf.replace(
file_resolution=options.registration.resolution,
iterations="100x100x100x50")))
for s_id, subj in subj_id_to_Subjec_for_within_dict.items() }
# create a montage image for each pair of time points
for s_id, output_from_intra in chain_xfms.items():
for time_pt_n, time_pt_n_plus_1, transform in output_from_intra[0]:
montage_chain = pipeline_montage_dir + "/quality_control_chain_ID_" + s_id + \
"_timepoint_" + str(time_pt_n) + "_to_" + str(time_pt_n_plus_1) + ".png"
chain_images = [transform.resampled, transform.target]
chain_verification_images = s.defer(create_quality_control_images(chain_images,
montage_output=montage_chain,
montage_dir=pipeline_montage_dir,
message="the alignment between ID " + s_id + " time point " +
str(time_pt_n) + " and " + str(time_pt_n_plus_1)))
if options.application.verbose:
print("\n\nTransformations gotten from the intrasubject registrations:")
for s_id, output_from_intra in chain_xfms.items():
print("ID: ", s_id)
for time_pt_n, time_pt_n_plus_1, transform in output_from_intra[0]:
print("Time point: ", time_pt_n, " to ", time_pt_n_plus_1, " trans: ", transform.xfm.path)
print("\n")
## stats
#
# The statistic files we want to create are the following:
# 1) subject <----- subject_common_time_point (resampled to common average)
# 2) subject <----- subject_common_time_point <- common_time_point_average (incorporates inter subject differences)
# 3) subject_time_point_n <----- subject_time_point_n+1 (resampled to common average)
# create transformation from each subject to the final common time point average,
# and from each subject to the subject's common time point
(non_rigid_xfms_to_common_avg, non_rigid_xfms_to_common_subj) = s.defer(get_chain_transforms_for_stats(subj_id_to_Subjec_for_within_dict,
intersubj_img_to_xfm_to_common_avg_dict,
chain_xfms))
# Ad 1) provide transformations from the subject's common time point to each subject
# These are temporary, because they still need to be resampled into the
# average common time point space
determinants_from_subject_common_to_subject = map_over_time_pt_dict_in_Subject(
lambda xfm: s.defer(determinants_at_fwhms(xfms=[s.defer(invert_xfmhandler(xfm))],
inv_xfms=[xfm], # determinants_at_fwhms now vectorized-unhelpful here
blur_fwhms=options.stats.stats_kernels)),
non_rigid_xfms_to_common_subj)
# the content of determinants_from_subject_common_to_subject is:
#
# {subject_ID : Subject(inter_subject_time_pt, time_pt_dict)
#
# where time_pt_dict contains:
#
# {time_point : Tuple(List[Tuple(float, Tuple(MincAtom, MincAtom))],
# List[Tuple(float, Tuple(MincAtom, MincAtom))])
#
# And to be a bit more verbose:
#
# {time_point : Tuple(relative_stat_files,
# absolute_stat_files)
#
# where either the relative_stat_files or the absolute_stat_files look like:
#
# [blur_kernel_1, (determinant_file_1, log_of_determinant_file_1),
# ...,
# blur_kernel_n, (determinant_file_n, log_of_determinant_file_n)]
#
# Now the only thing we really want to do, is to resample the actual log
# determinants that were generated into the space of the common average.
# To make that a little easier, I'll create a mapping that will contain:
#
# {subject_ID: Subject(intersubject_timepoint, {time_pt_1: [stat_file_1, ..., stat_file_n],
# ...,
# time_pt_n: [stat_file_1, ..., stat_file_n]}
# }
for s_id, subject_with_determinants in determinants_from_subject_common_to_subject.items():
transform_from_common_subj_to_common_avg = subj_ID_to_xfm_handler_to_common_avg[s_id].xfm
for time_pt, determinant_info in subject_with_determinants.time_pt_dict.items():
# here, each determinant_info is a DataFrame where each row contains
# 'abs_det', 'nlin_det', 'log_nlin_det', 'log_abs_det', 'fwhm' fields
# of the log-determinants, blurred at various fwhms (corresponding to different rows)
for _ix, row in determinant_info.iterrows():
for log_det_file_to_resample in (row.log_full_det, row.log_nlin_det):
# TODO the MincAtoms corresponding to the resampled files are never returned
new_name_wo_ext = log_det_file_to_resample.filename_wo_ext + "_resampled_to_common"
s.defer(mincresample(img=log_det_file_to_resample,
xfm=transform_from_common_subj_to_common_avg,
like=log_det_file_to_resample,
new_name_wo_ext=new_name_wo_ext,
subdir="stats-volumes"))
# Ad 2) provide transformations from the common avg to each subject. That's the
# inverse of what was provided by get_chain_transforms_for_stats()
determinants_from_common_avg_to_subject = map_over_time_pt_dict_in_Subject(
lambda xfm: s.defer(determinants_at_fwhms(xfms=[s.defer(invert_xfmhandler(xfm))],
inv_xfms=[xfm], # determinants_at_fwhms now vectorized-unhelpful here
blur_fwhms=options.stats.stats_kernels)),
non_rigid_xfms_to_common_avg)
# TODO don't just return an (unnamed-)tuple here
return Result(stages=s, output=Namespace(non_rigid_xfms_to_common=non_rigid_xfms_to_common_avg,
determinants_from_common_avg_to_subject=determinants_from_common_avg_to_subject,
determinants_from_subject_common_to_subject=determinants_from_subject_common_to_subject))
def maget(imgs : List[MincAtom], options, prefix, output_dir): # FIXME prefix, output_dir aren't used !!
s = Stages()
maget_options = options.maget.maget
resolution = options.registration.resolution # TODO or get_resolution_from_file(...) -- only if file always exists!
pipeline_sub_dir = os.path.join(options.application.output_directory,
options.application.pipeline_name + "_atlases")
if maget_options.atlas_lib is None:
raise ValueError("Need some atlases ...")
# TODO should alternately accept a CSV file ...
atlases = atlases_from_dir(atlas_lib=maget_options.atlas_lib,
max_templates=maget_options.max_templates,
pipeline_sub_dir=pipeline_sub_dir)
lsq12_conf = get_linear_configuration_from_options(options.maget.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=resolution)
nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.nlin.nlin_protocol,
reg_method=options.maget.nlin.reg_method,
file_resolution=resolution)
if maget_options.mask or maget_options.mask_only:
# this used to return alignments but doesn't currently do so
masked_img = s.defer(maget_mask(imgs=imgs,
maget_options=options.maget, atlases=atlases,
pipeline_sub_dir=pipeline_sub_dir + "_masking", # FIXME repeats all alignments!!!
resolution=resolution))
# now propagate only the masked form of the images and atlases:
imgs = masked_img
#atlases = masked_atlases # TODO is this needed?
if maget_options.mask_only:
# register each input to each atlas, creating a mask
return Result(stages=s, output=masked_img) # TODO rename `alignments` to `registrations`??
else:
if maget_options.mask:
del masked_img
# this `del` is just to verify that we don't accidentally use this later, since these potentially
# coarser alignments shouldn't be re-used (but if the protocols for masking and alignment are the same,
# hash-consing will take care of things), just the masked images they create; can be removed later
# if a sensible use is found
# images with labels from atlases
# N.B.: Even though we've already registered each image to each initial atlas, this happens again here,
# but using `nlin_hierarchy` instead of `masking_nlin_hierarchy` as options.
# This is not 'work-efficient' in the sense that this computation happens twice (although
# hopefully at greater precision the second time!), but the idea is to run a coarse initial
# registration to get a mask and then do a better registration with that mask (though I'm not
# sure exactly when this is faster than doing a single registration).
# This _can_ allow the overall computation to finish more rapidly
# (depending on the relative speed of the two alignment methods/parameters,
# number of atlases and other templates used, number of cores available, etc.).
atlas_labelled_imgs = (
pd.DataFrame({ 'img' : img,
'label_file' : s.defer( # can't use `label` in a pd.DataFrame index!
mincresample_new(img=atlas.labels,
xfm=s.defer(lsq12_nlin(source=img,
target=atlas,
lsq12_conf=lsq12_conf,
nlin_conf=nlin_hierarchy,
resample_source=False)).xfm,
like=img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range',)))}
for img in imgs for atlas in atlases)
)
if maget_options.pairwise:
def choose_new_templates(ts, n):
# currently silly, but we might implement a smarter method ...
# FIXME what if there aren't enough other imgs around?! This silently goes weird ...
return pd.Series(ts[:n+1]) # n+1 instead of n: choose one more since we won't use image as its own template ...
# FIXME: the --max-templates flag is ambiguously named ... should be --max-new-templates
# (and just use all atlases)
templates = pd.DataFrame({ 'template' : choose_new_templates(ts=imgs,
n=maget_options.max_templates - len(atlases))})
# note these images are the masked ones if masking was done ...
# the templates together with their (multiple) labels from the atlases (this merge just acts as a filter)
labelled_templates = pd.merge(left=atlas_labelled_imgs, right=templates,
left_on="img", right_on="template").drop('img', axis=1)
# images with new labels from the templates
imgs_and_templates = pd.merge(#left=atlas_labelled_imgs,
left=pd.DataFrame({ "img" : imgs }).assign(fake=1),
right=labelled_templates.assign(fake=1),
on='fake')
#left_on='img', right_on='template') # TODO do select here instead of below?
template_labelled_imgs = (
imgs_and_templates
.rename(columns={ 'label_file' : 'template_label_file' })
# don't register template to itself, since otherwise atlases would vote on that template twice
.select(lambda ix: imgs_and_templates.img[ix].path
!= imgs_and_templates.template[ix].path) # TODO hardcoded name
.assign(label_file=lambda df: df.apply(axis=1, func=lambda row:
s.defer(mincresample_new(img=row.template_label_file,
xfm=s.defer(lsq12_nlin(source=row.img,
target=row.template,
lsq12_conf=lsq12_conf,
nlin_conf=nlin_hierarchy,
resample_source=False)).xfm,
like=row.img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range',)))))
)
imgs_with_all_labels = pd.concat([atlas_labelled_imgs[['img', 'label_file']],
template_labelled_imgs[['img', 'label_file']]],
ignore_index=True)
else:
imgs_with_all_labels = atlas_labelled_imgs
segmented_imgs = (
imgs_with_all_labels
.groupby('img')
.aggregate({'label_file' : lambda resampled_label_files: list(resampled_label_files)})
.rename(columns={ 'label_file' : 'label_files' })
.reset_index()
.assign(voted_labels=lambda df: df.apply(axis=1, func=lambda row:
s.defer(voxel_vote(label_files=row.label_files,
output_dir=os.path.join(row.img.pipeline_sub_dir, row.img.output_sub_dir)))))
.apply(axis=1, func=lambda row: row.img._replace(labels=row.voted_labels))
)
return Result(stages=s, output=segmented_imgs)
def maget_mask(imgs : List[MincAtom], maget_options, resolution : float, pipeline_sub_dir : str, atlases=None):
s = Stages()
resample = np.vectorize(mincresample_new, excluded={"extra_flags"})
defer = np.vectorize(s.defer)
original_imgs = imgs
imgs = copy.deepcopy(imgs)
original_imgs = pd.Series(original_imgs, index=[img.path for img in original_imgs])
for img in imgs:
img.output_sub_dir = os.path.join(img.output_sub_dir, "masking")
# TODO dereference maget_options -> maget_options.maget outside maget_mask call?
if atlases is None:
if maget_options.maget.atlas_lib is None:
raise ValueError("need some atlases for MAGeT-based masking ...")
atlases = atlases_from_dir(atlas_lib=maget_options.maget.atlas_lib,
max_templates=maget_options.maget.max_templates,
pipeline_sub_dir=pipeline_sub_dir)
lsq12_conf = get_linear_configuration_from_options(maget_options.lsq12,
LinearTransType.lsq12,
resolution)
masking_nlin_hierarchy = get_nonlinear_configuration_from_options(maget_options.maget.masking_nlin_protocol,
maget_options.maget.mask_method,
resolution)
# TODO lift outside then delete
#masking_imgs = copy.deepcopy(imgs)
#for img in masking_imgs:
# img.pipeline_sub_dir = os.path.join(img.pipeline_sub_dir, "masking")
masking_alignments = pd.DataFrame({ 'img' : img,
'atlas' : atlas,
'xfm' : s.defer(lsq12_nlin(source=img, target=atlas,
lsq12_conf=lsq12_conf,
nlin_conf=masking_nlin_hierarchy,
resample_source=False))}
for img in imgs for atlas in atlases)
# propagate a mask to each image using the above `alignments` as follows:
# - for each image, voxel_vote on the masks propagated to that image to get a suitable mask
# - run mincmath -clobber -mult <img> <voted_mask> to apply the mask to the files
masked_img = (
masking_alignments
.assign(resampled_mask=lambda df: defer(resample(img=df.atlas.apply(lambda x: x.mask),
xfm=df.xfm.apply(lambda x: x.xfm),
like=df.img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
postfix="-input-mask",
subdir="tmp",
# TODO annoying hack; fix mincresample(_mask) ...:
#new_name_wo_ext=df.apply(lambda row:
# "%s_to_%s-input-mask" % (row.atlas.filename_wo_ext,
# row.img.filename_wo_ext),
# axis=1),
extra_flags=("-keep_real_range",))))
.groupby('img', as_index=False)
.aggregate({'resampled_mask' : lambda masks: list(masks)})
.rename(columns={"resampled_mask" : "resampled_masks"})
.assign(voted_mask=lambda df: df.apply(axis=1,
func=lambda row:
s.defer(mincmath(op="max", vols=sorted(row.resampled_masks),
new_name="%s_max_mask" % row.img.filename_wo_ext,
subdir="tmp"))))
.apply(axis=1, func=lambda row: row.img._replace(mask=row.voted_mask)))
# resample the atlas images back to the input images:
# (note: this doesn't modify `masking_alignments`, but only stages additional outputs)
masking_alignments.assign(resampled_img=lambda df:
defer(resample(img=df.atlas,
xfm=df.xfm.apply(lambda x: x.xfm),
subdir="tmp",
# TODO delete this stupid hack:
#new_name_wo_ext=df.apply(lambda row:
# "%s_to_%s-resampled" % (row.atlas.filename_wo_ext,
# row.img.filename_wo_ext),
# axis=1),
like=df.img, invert=True)))
for img in masked_img:
img.output_sub_dir = original_imgs.ix[img.path].output_sub_dir
return Result(stages=s, output=masked_img)
