def maget(imgs: List[MincAtom],
options,
prefix,
output_dir,
build_model_xfms=None):
# 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")
atlases = get_atlases(maget_options, pipeline_sub_dir)
lsq12_conf = get_linear_configuration_from_options(
options.maget.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=resolution)
nlin_component = get_nonlinear_component(options.maget.nlin.reg_method)
# TODO should this be here or outside `maget` call?
#imgs = [s.defer(nlin_component.ToMinc.from_mnc(img)) for img in imgs]
#nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.nlin.nlin_protocol,
# next(iter(options.maget.nlin.flags_.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,
nlin_module=nlin_component,
lsq12_conf=lsq12_conf,
nlin_options=options.maget.nlin.nlin_protocol,
resolution=resolution,
#nlin_conf=nlin_hierarchy,
resample_source=False)).xfm,
like=img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range', '-labels')))
} 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)
# TODO we could have a separate templates_csv (or --template-files f [f ...]) but you can just
# run a separate MAGeT pipeline and
#if maget_options.templates_csv:
# templates = pd.read_csv(maget_options.templates_csv).template
#else:
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?
#if build_model_xfms is not None:
# # use path instead of full mincatom as key in case we're reading these in from a CSV:
# xfm_dict = { x.source.path : x.xfm for x in build_model_xfms }
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
.loc[lambda df: df.index.map(lambda ix: df.img[ix].path != df.
template[ix].path)].
assign(label_file=lambda df: df.apply(
axis=1,
func=lambda row: s.defer(
# TODO switch to uses of nlin_component.whatever(...) in several places below?
mincresample_new(
#nlin_component.Algorithms.resample(
img=row.template_label_file,
xfm=s.defer(
lsq12_nlin(
source=row.img,
target=row.template,
lsq12_conf=lsq12_conf,
resolution=resolution,
nlin_module=nlin_component,
nlin_options=options.maget.nlin.
nlin_protocol,
#nlin_conf=nlin_hierarchy,
resample_source=False)).xfm
if build_model_xfms is None
# use transforms from model building if we have them:
else s.defer(
xfmconcat(
#nlin_component.Algorithms.concat(
[
build_model_xfms[row.img.path],
s.defer(
xfminvert(
#nlin_component.Algorithms.invert(
build_model_xfms[row.template.
path],
subdir="tmp"))
])),
like=row.img,
invert=True,
#use_nn_interpolation=True
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range', '-labels')))))
) if len(imgs) > 1 else pd.DataFrame({
'img': [],
'label_file': []
})
# ... as no distinct templates to align if only one image supplied (#320)
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
#imgs_with_all_labels = imgs_with_all_labels.applymap(
# lambda x: s.defer(nlin_component.ToMinc.to_mnc(x)))
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),
name=row.img.filename_wo_ext + "_voted"))
)).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], 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, build_model_xfms=None):
# 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")
atlases = get_atlases(maget_options, pipeline_sub_dir)
lsq12_conf = get_linear_configuration_from_options(options.maget.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=resolution)
nlin_component = get_nonlinear_component(options.maget.nlin.reg_method)
# TODO should this be here or outside `maget` call?
#imgs = [s.defer(nlin_component.ToMinc.from_mnc(img)) for img in imgs]
#nlin_hierarchy = get_nonlinear_configuration_from_options(options.maget.nlin.nlin_protocol,
# next(iter(options.maget.nlin.flags_.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,
nlin_module=nlin_component,
lsq12_conf=lsq12_conf,
nlin_options=options.maget.nlin.nlin_protocol,
resolution=resolution,
#nlin_conf=nlin_hierarchy,
resample_source=False)).xfm,
like=img,
invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range', '-labels')))}
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)
# TODO we could have a separate templates_csv (or --template-files f [f ...]) but you can just
# run a separate MAGeT pipeline and
#if maget_options.templates_csv:
# templates = pd.read_csv(maget_options.templates_csv).template
#else:
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?
#if build_model_xfms is not None:
# # use path instead of full mincatom as key in case we're reading these in from a CSV:
# xfm_dict = { x.source.path : x.xfm for x in build_model_xfms }
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
.loc[lambda df: df.index.map(lambda ix: df.img[ix].path
!= df.template[ix].path)]
.assign(label_file=lambda df: df.apply(axis=1, func=lambda row:
s.defer(
# TODO switch to uses of nlin_component.whatever(...) in several places below?
mincresample_new(
#nlin_component.Algorithms.resample(
img=row.template_label_file,
xfm=s.defer(
lsq12_nlin(source=row.img,
target=row.template,
lsq12_conf=lsq12_conf,
resolution=resolution,
nlin_module=nlin_component,
nlin_options=options.maget.nlin.nlin_protocol,
#nlin_conf=nlin_hierarchy,
resample_source=False)).xfm
if build_model_xfms is None
# use transforms from model building if we have them:
else s.defer(
xfmconcat(
#nlin_component.Algorithms.concat(
[build_model_xfms[row.img.path],
s.defer(
xfminvert(
#nlin_component.Algorithms.invert(
build_model_xfms[row.template.path],
subdir="tmp"))])),
like=row.img,
invert=True,
#use_nn_interpolation=True
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range', '-labels')
))))
) if len(imgs) > 1 else pd.DataFrame({ 'img' : [], 'label_file' : [] })
# ... as no distinct templates to align if only one image supplied (#320)
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
#imgs_with_all_labels = imgs_with_all_labels.applymap(
# lambda x: s.defer(nlin_component.ToMinc.to_mnc(x)))
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),
name=row.img.filename_wo_ext+"_voted"))))
.apply(axis=1, func=lambda row: row.img._replace(labels=row.voted_labels))
)
return Result(stages=s, output=segmented_imgs)
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 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)
