def make_labels_level_img(img_path, level, prefix=None, show=False):
"""Replace labels in an image with their parents at the given level.
Labels that do not fall within a parent at that level will remain in place.
Args:
img_path: Path to the base image from which the corresponding
registered image will be found.
level: Ontological level at which to group child labels.
prefix: Start of path for output image; defaults to None to
use ``img_path`` instead.
show: True to show the images after generating them; defaults to False.
"""
# load original labels image and setup ontology dictionary
labels_sitk = sitk_io.load_registered_img(img_path,
config.RegNames.IMG_LABELS.value,
get_sitk=True)
labels_np = sitk.GetArrayFromImage(labels_sitk)
ref = ontology.load_labels_ref(config.load_labels)
labels_ref_lookup = ontology.create_aba_reverse_lookup(ref)
ids = list(labels_ref_lookup.keys())
for key in ids:
keys = [key, -1 * key]
for region in keys:
if region == 0: continue
# get ontological label
label = labels_ref_lookup[abs(region)]
label_level = label[ontology.NODE][config.ABAKeys.LEVEL.value]
if label_level == level:
# get children (including parent first) at given level
# and replace them with parent
label_ids = ontology.get_children_from_id(
labels_ref_lookup, region)
labels_region = np.isin(labels_np, label_ids)
print("replacing labels within", region)
labels_np[labels_region] = region
labels_level_sitk = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_np)
# generate an edge image at this level
labels_edge = vols.make_labels_edge(labels_np)
labels_edge_sikt = sitk_io.replace_sitk_with_numpy(labels_sitk,
labels_edge)
# write and optionally display labels level image
imgs_write = {
config.RegNames.IMG_LABELS_LEVEL.value.format(level):
labels_level_sitk,
config.RegNames.IMG_LABELS_EDGE_LEVEL.value.format(level):
labels_edge_sikt,
}
out_path = prefix if prefix else img_path
sitk_io.write_reg_images(imgs_write, out_path)
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
def make_labels_level_img(img_path, level, prefix=None, show=False):
"""Replace labels in an image with their parents at the given level.
Labels that do not fall within a parent at that level will remain in place.
Args:
img_path: Path to the base image from which the corresponding
registered image will be found.
level: Ontological level at which to group child labels.
prefix: Start of path for output image; defaults to None to
use ``img_path`` instead.
show: True to show the images after generating them; defaults to False.
"""
# load original labels image and setup ontology dictionary
labels_sitk = sitk_io.load_registered_img(img_path,
config.RegNames.IMG_LABELS.value,
get_sitk=True)
labels_np = sitk.GetArrayFromImage(labels_sitk)
ref = ontology.LabelsRef(config.load_labels).load()
# remap labels to given level
labels_np = ontology.make_labels_level(labels_np, ref, level)
labels_level_sitk = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_np)
# generate an edge image at this level
labels_edge = vols.make_labels_edge(labels_np)
labels_edge_sikt = sitk_io.replace_sitk_with_numpy(labels_sitk,
labels_edge)
# write and optionally display labels level image
imgs_write = {
config.RegNames.IMG_LABELS_LEVEL.value.format(level):
labels_level_sitk,
config.RegNames.IMG_LABELS_EDGE_LEVEL.value.format(level):
labels_edge_sikt,
}
out_path = prefix if prefix else img_path
sitk_io.write_reg_images(imgs_write, out_path)
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
def make_labels_diff_img(img_path, df_path, meas, fn_avg, prefix=None,
show=False, level=None, meas_path_name=None,
col_wt=None):
"""Replace labels in an image with the differences in metrics for
each given region between two conditions.
Args:
img_path: Path to the base image from which the corresponding
registered image will be found.
df_path: Path to data frame with metrics for the labels.
meas: Name of colum in data frame with the chosen measurement.
fn_avg: Function to apply to the set of measurements, such as a mean.
Can be None if ``df_path`` points to a stats file from which
to extract metrics directly in :meth:``vols.map_meas_to_labels``.
prefix: Start of path for output image; defaults to None to
use ``img_path`` instead.
show: True to show the images after generating them; defaults to False.
level: Ontological level at which to look up and show labels.
Assume that labels level image corresponding to this value
has already been generated by :meth:``make_labels_level_img``.
Defaults to None to use only drawn labels.
meas_path_name: Name to use in place of `meas` in output path;
defaults to None.
col_wt (str): Name of column to use for weighting; defaults to None.
"""
# load labels image and data frame before generating map for the
# given metric of the chosen measurement
print("Generating labels difference image for", meas, "from", df_path)
reg_name = (config.RegNames.IMG_LABELS.value if level is None
else config.RegNames.IMG_LABELS_LEVEL.value.format(level))
labels_sitk = sitk_io.load_registered_img(img_path, reg_name, get_sitk=True)
labels_np = sitk.GetArrayFromImage(labels_sitk)
df = pd.read_csv(df_path)
labels_diff = vols.map_meas_to_labels(
labels_np, df, meas, fn_avg, reverse=True, col_wt=col_wt)
if labels_diff is None: return
labels_diff_sitk = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_diff)
# save and show labels difference image using measurement name in
# output path or overriding with custom name
meas_path = meas if meas_path_name is None else meas_path_name
reg_diff = libmag.insert_before_ext(
config.RegNames.IMG_LABELS_DIFF.value, meas_path, "_")
if fn_avg is not None:
# add function name to output path if given
reg_diff = libmag.insert_before_ext(
reg_diff, fn_avg.__name__, "_")
imgs_write = {reg_diff: labels_diff_sitk}
out_path = prefix if prefix else img_path
sitk_io.write_reg_images(imgs_write, out_path)
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
def make_sub_segmented_labels(img_path, suffix=None):
"""Divide each label based on anatomical borders to create a
sub-segmented image.
The segmented labels image will be loaded, or if not available, the
non-segmented labels will be loaded instead.
Args:
img_path: Path to main image from which registered images will
be loaded.
suffix: Modifier to append to end of ``img_path`` basename for
registered image files that were output to a modified name;
defaults to None.
Returns:
Sub-segmented image as a Numpy array of the same shape as
the image at ``img_path``.
"""
# adjust image path with suffix
mod_path = img_path
if suffix is not None:
mod_path = libmag.insert_before_ext(mod_path, suffix)
# load labels
labels_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
# atlas edge image is associated with original, not modified image
atlas_edge = sitk_io.load_registered_img(
img_path, config.RegNames.IMG_ATLAS_EDGE.value)
# sub-divide the labels and save to file
labels_img_np = sitk.GetArrayFromImage(labels_sitk)
labels_subseg = segmenter.sub_segment_labels(labels_img_np, atlas_edge)
labels_subseg_sitk = sitk_io.replace_sitk_with_numpy(
labels_sitk, labels_subseg)
sitk_io.write_reg_images(
{config.RegNames.IMG_LABELS_SUBSEG.value: labels_subseg_sitk}, mod_path)
return labels_subseg
def make_edge_images(path_img, show=True, atlas=True, suffix=None,
path_atlas_dir=None):
"""Make edge-detected atlas and associated labels images.
The atlas is assumed to be a sample (eg microscopy) image on which
an edge-detection filter will be applied. The labels image is
assumed to be an annotated image whose edges will be found by
obtaining the borders of all separate labels.
Args:
path_img: Path to the image atlas. The labels image will be
found as a corresponding, registered image, unless
``path_atlas_dir`` is given.
show (bool): True if the output images should be displayed; defaults
to True.
atlas: True if the primary image is an atlas, which is assumed
to be symmetrical. False if the image is an experimental/sample
image, in which case erosion will be performed on the full
images, and stats will not be performed.
suffix: Modifier to append to end of ``path_img`` basename for
registered image files that were output to a modified name;
defaults to None.
path_atlas_dir: Path to atlas directory to use labels from that
directory rather than from labels image registered to
``path_img``, such as when the sample image is registered
to an atlas rather than the other way around. Typically
coupled with ``suffix`` to compare same sample against
different labels. Defaults to None.
"""
# load intensity image from which to detect edges
atlas_suffix = config.reg_suffixes[config.RegSuffixes.ATLAS]
if not atlas_suffix:
if atlas:
# atlases default to using the atlas volume image
print("generating edge images for atlas")
atlas_suffix = config.RegNames.IMG_ATLAS.value
else:
# otherwise, use the experimental image
print("generating edge images for experiment/sample image")
atlas_suffix = config.RegNames.IMG_EXP.value
# adjust image path with suffix
mod_path = path_img
if suffix is not None:
mod_path = libmag.insert_before_ext(mod_path, suffix)
labels_from_atlas_dir = path_atlas_dir and os.path.isdir(path_atlas_dir)
if labels_from_atlas_dir:
# load labels from atlas directory
# TODO: consider applying suffix to labels dir
path_atlas = path_img
path_labels = os.path.join(
path_atlas_dir, config.RegNames.IMG_LABELS.value)
print("loading labels from", path_labels)
labels_sitk = sitk.ReadImage(path_labels)
else:
# load labels registered to sample image
path_atlas = mod_path
labels_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_img_np = sitk.GetArrayFromImage(labels_sitk)
# load atlas image, set resolution from it
atlas_sitk = sitk_io.load_registered_img(
path_atlas, atlas_suffix, get_sitk=True)
config.resolutions = np.array([atlas_sitk.GetSpacing()[::-1]])
atlas_np = sitk.GetArrayFromImage(atlas_sitk)
# output images
atlas_sitk_log = None
atlas_sitk_edge = None
labels_sitk_interior = None
log_sigma = config.atlas_profile["log_sigma"]
if log_sigma is not None and suffix is None:
# generate LoG and edge-detected images for original image
print("generating LoG edge-detected images with sigma", log_sigma)
thresh = (config.atlas_profile["atlas_threshold"]
if config.atlas_profile["log_atlas_thresh"] else None)
atlas_log = cv_nd.laplacian_of_gaussian_img(
atlas_np, sigma=log_sigma, labels_img=labels_img_np, thresh=thresh)
atlas_sitk_log = sitk_io.replace_sitk_with_numpy(atlas_sitk, atlas_log)
atlas_edge = cv_nd.zero_crossing(atlas_log, 1).astype(np.uint8)
atlas_sitk_edge = sitk_io.replace_sitk_with_numpy(
atlas_sitk, atlas_edge)
else:
# if sigma not set or if using suffix to compare two images,
# load from original image to compare against common image
atlas_edge = sitk_io.load_registered_img(
path_img, config.RegNames.IMG_ATLAS_EDGE.value)
erode = config.atlas_profile["erode_labels"]
if erode["interior"]:
# make map of label interiors for interior/border comparisons
print("Eroding labels to generate interior labels image")
erosion = config.atlas_profile[
profiles.RegKeys.EDGE_AWARE_REANNOTATION]
erosion_frac = config.atlas_profile["erosion_frac"]
interior, _ = erode_labels(
labels_img_np, erosion, erosion_frac,
atlas and _is_profile_mirrored(), _get_mirror_mult())
labels_sitk_interior = sitk_io.replace_sitk_with_numpy(
labels_sitk, interior)
# make labels edge and edge distance images
dist_to_orig, labels_edge = edge_distances(
labels_img_np, atlas_edge, spacing=atlas_sitk.GetSpacing()[::-1])
dist_sitk = sitk_io.replace_sitk_with_numpy(atlas_sitk, dist_to_orig)
labels_sitk_edge = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_edge)
# show all images
imgs_write = {
config.RegNames.IMG_ATLAS_LOG.value: atlas_sitk_log,
config.RegNames.IMG_ATLAS_EDGE.value: atlas_sitk_edge,
config.RegNames.IMG_LABELS_EDGE.value: labels_sitk_edge,
config.RegNames.IMG_LABELS_INTERIOR.value: labels_sitk_interior,
config.RegNames.IMG_LABELS_DIST.value: dist_sitk,
}
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
# write images to same directory as atlas with appropriate suffix
sitk_io.write_reg_images(imgs_write, mod_path)
def merge_atlas_segmentations(img_paths, show=True, atlas=True, suffix=None):
"""Merge atlas segmentations for a list of files as a multiprocessing
wrapper for :func:``merge_atlas_segmentations``, after which
edge image post-processing is performed separately since it
contains tasks also performed in multiprocessing.
Args:
img_paths (List[str]): Sequence of image paths to load.
show (bool): True if the output images should be displayed; defaults
to True.
atlas (bool): True if the image is an atlas; defaults to True.
suffix (str): Modifier to append to end of ``img_path`` basename for
registered image files that were output to a modified name;
defaults to None.
"""
start_time = time()
# erode all labels images into markers for watershed; not multiprocessed
# since erosion is itself multiprocessed
erode = config.atlas_profile["erode_labels"]
erosion = config.atlas_profile[profiles.RegKeys.EDGE_AWARE_REANNOTATION]
erosion_frac = config.atlas_profile["erosion_frac"]
mirrored = atlas and _is_profile_mirrored()
mirror_mult = _get_mirror_mult()
dfs_eros = []
for img_path in img_paths:
mod_path = img_path
if suffix is not None:
mod_path = libmag.insert_before_ext(mod_path, suffix)
labels_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
print("Eroding labels to generate markers for atlas segmentation")
df = None
if erode["markers"]:
# use default minimal post-erosion size (not setting erosion frac)
markers, df = erode_labels(
sitk.GetArrayFromImage(labels_sitk), erosion,
mirrored=mirrored, mirror_mult=mirror_mult)
labels_sitk_markers = sitk_io.replace_sitk_with_numpy(
labels_sitk, markers)
sitk_io.write_reg_images(
{config.RegNames.IMG_LABELS_MARKERS.value: labels_sitk_markers},
mod_path)
df_io.data_frames_to_csv(
df, "{}_markers.csv".format(os.path.splitext(mod_path)[0]))
dfs_eros.append(df)
pool = chunking.get_mp_pool()
pool_results = []
for img_path, df in zip(img_paths, dfs_eros):
print("setting up atlas segmentation merge for", img_path)
# convert labels image into markers
exclude = df.loc[
np.isnan(df[config.SmoothingMetrics.FILTER_SIZE.value]),
config.AtlasMetrics.REGION.value]
print("excluding these labels from re-segmentation:\n", exclude)
pool_results.append(pool.apply_async(
edge_aware_segmentation,
args=(img_path, show, atlas, suffix, exclude, mirror_mult)))
for result in pool_results:
# edge distance calculation and labels interior image generation
# are multiprocessed, so run them as post-processing tasks to
# avoid nested multiprocessing
path = result.get()
mod_path = path
if suffix is not None:
mod_path = libmag.insert_before_ext(path, suffix)
# make edge distance images and stats
labels_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_np = sitk.GetArrayFromImage(labels_sitk)
dist_to_orig, labels_edge = edge_distances(
labels_np, path=path, spacing=labels_sitk.GetSpacing()[::-1])
dist_sitk = sitk_io.replace_sitk_with_numpy(labels_sitk, dist_to_orig)
labels_sitk_edge = sitk_io.replace_sitk_with_numpy(
labels_sitk, labels_edge)
labels_sitk_interior = None
if erode["interior"]:
# make interior images from labels using given targeted
# post-erosion frac
interior, _ = erode_labels(
labels_np, erosion, erosion_frac=erosion_frac,
mirrored=mirrored, mirror_mult=mirror_mult)
labels_sitk_interior = sitk_io.replace_sitk_with_numpy(
labels_sitk, interior)
# write images to same directory as atlas
imgs_write = {
config.RegNames.IMG_LABELS_DIST.value: dist_sitk,
config.RegNames.IMG_LABELS_EDGE.value: labels_sitk_edge,
config.RegNames.IMG_LABELS_INTERIOR.value: labels_sitk_interior,
}
sitk_io.write_reg_images(imgs_write, mod_path)
if show:
for img in imgs_write.values():
if img: sitk.Show(img)
print("finished {}".format(path))
pool.close()
pool.join()
print("time elapsed for merging atlas segmentations:", time() - start_time)
def edge_aware_segmentation(path_atlas, show=True, atlas=True, suffix=None,
exclude_labels=None, mirror_mult=-1):
"""Segment an atlas using its previously generated edge map.
Labels may not match their own underlying atlas image well,
particularly in the orthogonal directions in which the labels
were not constructed. To improve alignment between the labels
and the atlas itself, register the labels to an automated, roughly
segmented version of the atlas. The goal is to improve the
labels' alignment so that the atlas/labels combination can be
used for another form of automated segmentation by registering
them to experimental brains via :func:``register``.
Edge files are assumed to have been generated by
:func:``make_edge_images``.
Args:
path_atlas (str): Path to the fixed file, typically the atlas file
with stained sections. The corresponding edge and labels
files will be loaded based on this path.
show (bool): True if the output images should be displayed; defaults
to True.
atlas (bool): True if the primary image is an atlas, which is assumed
to be symmetrical. False if the image is an experimental/sample
image, in which case segmentation will be performed on the full
images, and stats will not be performed.
suffix (str): Modifier to append to end of ``path_atlas`` basename for
registered image files that were output to a modified name;
defaults to None. If ``atlas`` is True, ``suffix`` will only
be applied to saved files, with files still loaded based on the
original path.
exclude_labels (List[int]): Sequence of labels to exclude from the
segmentation; defaults to None.
mirror_mult (int): Multiplier for mirrored labels; defaults to -1
to make mirrored labels the inverse of their source labels.
"""
# adjust image path with suffix
load_path = path_atlas
mod_path = path_atlas
if suffix is not None:
mod_path = libmag.insert_before_ext(mod_path, suffix)
if atlas: load_path = mod_path
# load corresponding files via SimpleITK
atlas_sitk = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_ATLAS.value, get_sitk=True)
atlas_sitk_edge = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_ATLAS_EDGE.value, get_sitk=True)
labels_sitk = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_sitk_markers = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_LABELS_MARKERS.value, get_sitk=True)
# get Numpy arrays of images
atlas_img_np = sitk.GetArrayFromImage(atlas_sitk)
atlas_edge = sitk.GetArrayFromImage(atlas_sitk_edge)
labels_img_np = sitk.GetArrayFromImage(labels_sitk)
markers = sitk.GetArrayFromImage(labels_sitk_markers)
# segment image from markers
sym_axis = atlas_refiner.find_symmetric_axis(atlas_img_np)
mirrorred = atlas and sym_axis >= 0
len_half = None
seg_args = {"exclude_labels": exclude_labels}
edge_prof = config.atlas_profile[profiles.RegKeys.EDGE_AWARE_REANNOTATION]
if edge_prof:
edge_filt = edge_prof[profiles.RegKeys.WATERSHED_MASK_FILTER]
if edge_filt and len(edge_filt) > 1:
# watershed mask filter settings from atlas profile
seg_args["mask_filt"] = edge_filt[0]
seg_args["mask_filt_size"] = edge_filt[1]
if mirrorred:
# segment only half of image, assuming symmetry
len_half = atlas_img_np.shape[sym_axis] // 2
slices = [slice(None)] * labels_img_np.ndim
slices[sym_axis] = slice(len_half)
sl = tuple(slices)
labels_seg = segmenter.segment_from_labels(
atlas_edge[sl], markers[sl], labels_img_np[sl], **seg_args)
else:
# segment the full image, including excluded labels on the opposite side
exclude_labels = exclude_labels.tolist().extend(
(mirror_mult * exclude_labels).tolist())
seg_args["exclude_labels"] = exclude_labels
labels_seg = segmenter.segment_from_labels(
atlas_edge, markers, labels_img_np, **seg_args)
smoothing = config.atlas_profile["smooth"]
if smoothing is not None:
# smoothing by opening operation based on profile setting
atlas_refiner.smooth_labels(
labels_seg, smoothing, config.SmoothingModes.opening)
if mirrorred:
# mirror back to other half
labels_seg = _mirror_imported_labels(
labels_seg, len_half, mirror_mult, sym_axis)
# expand background to smoothed background of original labels to
# roughly match background while still allowing holes to be filled
crop = config.atlas_profile["crop_to_orig"]
atlas_refiner.crop_to_orig(
labels_img_np, labels_seg, crop)
if labels_seg.dtype != labels_img_np.dtype:
# watershed may give different output type, so cast back if so
labels_seg = labels_seg.astype(labels_img_np.dtype)
labels_sitk_seg = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_seg)
# show DSCs for labels
print("\nMeasuring overlap of atlas and combined watershed labels:")
atlas_refiner.measure_overlap_combined_labels(atlas_sitk, labels_sitk_seg)
print("Measuring overlap of individual original and watershed labels:")
atlas_refiner.measure_overlap_labels(labels_sitk, labels_sitk_seg)
print("\nMeasuring overlap of combined original and watershed labels:")
atlas_refiner.measure_overlap_labels(
atlas_refiner.make_labels_fg(labels_sitk),
atlas_refiner.make_labels_fg(labels_sitk_seg))
print()
# show and write image to same directory as atlas with appropriate suffix
sitk_io.write_reg_images(
{config.RegNames.IMG_LABELS.value: labels_sitk_seg}, mod_path)
if show: sitk.Show(labels_sitk_seg)
return path_atlas
def make_density_image(
img_path: str,
scale: Optional[float] = None,
shape: Optional[Sequence[int]] = None,
suffix: Optional[str] = None,
labels_img_sitk: Optional[sitk.Image] = None,
channel: Optional[Sequence[int]] = None,
matches: Dict[Tuple[int, int], "colocalizer.BlobMatch"] = None,
atlas_profile: Optional["atlas_prof.AtlasProfile"] = None
) -> Tuple[np.ndarray, str]:
"""Make a density image based on associated blobs.
Uses the size and resolutions of the original image stores in the blobs
if available to determine scaling between the blobs and the output image.
Otherwise, uses the shape of the registered labels image to set
the voxel sizes for the blobs.
If ``matches`` is given, a heat map will be generated for each set
of channels given in the dictionary. Otherwise, if the loaded blobs
file has intensity-based colocalizations, a heat map will be generated
for each combination of channels.
Args:
img_path: Path to image, which will be used to indentify the blobs file.
scale: Scaling factor between the blobs' space and the output space;
defaults to None to use the register. Scaling is found by
:meth:`magmap.np_io.find_scaling`.
shape: Output shape, used for scaling; defaults to None.
suffix: Modifier to append to end of ``img_path`` basename for
registered image files that were output to a modified name;
defaults to None.
labels_img_sitk: Labels image; defaults to None to load from a
registered labels image.
channel: Sequence of channels to include in density image. For
multiple channels, blobs from all these channels are combined
into one heatmap. Defaults to None to use all channels.
matches: Dictionary of channel combinations to blob matches; defaults
to None.
atlas_profile: Atlas profile, used for scaling; defaults to None.
Returns:
Tuple of the density image as a Numpy array in the
same shape as the opened image and the original and ``img_path``
to track such as for multiprocessing.
"""
def make_heat_map():
# build heat map to store densities per label px and save to file
coord_scaled = ontology.scale_coords(blobs_chl[:, :3], scaling,
labels_img.shape)
_logger.debug("Scaled coords:\n%s", coord_scaled)
return cv_nd.build_heat_map(labels_img.shape, coord_scaled)
# set up paths and get labels image
_logger.info("\n\nGenerating heat map from blobs")
mod_path = img_path
if suffix is not None:
mod_path = libmag.insert_before_ext(img_path, suffix)
# load blobs
blobs = detector.Blobs().load_blobs(np_io.img_to_blobs_path(img_path))
is_2d = False
if (shape is not None and blobs.roi_size is not None
and blobs.resolutions is not None):
# prepare output image and scaling factor from it to the blobs
scaling = np.divide(shape, blobs.roi_size)
labels_spacing = np.divide(blobs.resolutions[0], scaling)
labels_img = np.zeros(shape, dtype=np.uint8)
labels_img_sitk = sitk.GetImageFromArray(labels_img)
labels_img_sitk.SetSpacing(labels_spacing[::-1])
else:
# default to use labels image as the size of the output image
if labels_img_sitk is None:
labels_img_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_img = sitk.GetArrayFromImage(labels_img_sitk)
is_2d = labels_img.ndim == 2
if is_2d:
# temporarily convert 2D images to 3D
labels_img = labels_img[None]
# find the scaling between the blobs and the labels image
target_size = (None if atlas_profile is None else
atlas_profile["target_size"])
scaling = np_io.find_scaling(img_path, labels_img.shape, scale,
target_size)[0]
if shape is not None:
# scale blob coordinates and heat map to an alternative final shape
scaling = np.divide(shape, np.divide(labels_img.shape, scaling))
labels_spacing = np.multiply(labels_img_sitk.GetSpacing()[::-1],
np.divide(labels_img.shape, shape))
labels_img = np.zeros(shape, dtype=labels_img.dtype)
labels_img_sitk.SetSpacing(labels_spacing[::-1])
_logger.debug("Using image scaling: {}".format(scaling))
# annotate blobs based on position
blobs_chl = blobs.blobs
if channel is not None:
_logger.info(
"Using blobs from channel(s), combining if multiple channels: %s",
channel)
blobs_chl = blobs_chl[np.isin(
detector.Blobs.get_blobs_channel(blobs_chl), channel)]
heat_map = make_heat_map()
if is_2d:
# convert back to 3D
heat_map = heat_map[0]
imgs_write = {
config.RegNames.IMG_HEAT_MAP.value:
sitk_io.replace_sitk_with_numpy(labels_img_sitk, heat_map)
}
heat_colocs = None
if matches:
# create heat maps for match-based colocalization combos
heat_colocs = []
for chl_combo, chl_matches in matches.items():
_logger.info(
"Generating match-based colocalization heat map "
"for channel combo: %s", chl_combo)
# use blobs in first channel of each channel pair for simplicity
blobs_chl = chl_matches.get_blobs(1)
heat_colocs.append(make_heat_map())
elif blobs.colocalizations is not None:
# create heat map for each intensity-based colocalization combo
# as a separate channel in output image
blob_chls = range(blobs.colocalizations.shape[1])
blob_chls_len = len(blob_chls)
if blob_chls_len > 1:
# get all channel combos that include given channels
combos = []
chls = blob_chls if channel is None else channel
for r in range(2, blob_chls_len + 1):
combos.extend([
tuple(c) for c in itertools.combinations(blob_chls, r)
if all([h in c for h in chls])
])
heat_colocs = []
for combo in combos:
_logger.info(
"Generating intensity-based colocalization heat map "
"for channel combo: %s", combo)
blobs_chl = blobs.blobs[np.all(np.equal(
blobs.colocalizations[:, combo], 1),
axis=1)]
heat_colocs.append(make_heat_map())
if heat_colocs is not None:
# combine heat maps into single image
heat_colocs = np.stack(heat_colocs, axis=3)
imgs_write[config.RegNames.IMG_HEAT_COLOC.value] = \
sitk_io.replace_sitk_with_numpy(
labels_img_sitk, heat_colocs)
# write images to file
sitk_io.write_reg_images(imgs_write, mod_path)
return heat_map, img_path
def make_density_image(img_path,
scale=None,
shape=None,
suffix=None,
labels_img_sitk=None):
"""Make a density image based on associated blobs.
Uses the shape of the registered labels image by default to set
the voxel sizes for the blobs.
Args:
img_path: Path to image, which will be used to indentify the blobs file.
scale: Rescaling factor as a scalar value to find the corresponding
full-sized image. Defaults to None to use the register
setting ``target_size`` instead if available, falling back
to load the full size image to find its shape if necessary.
shape: Final shape size; defaults to None to use the shape of
the labels image.
suffix: Modifier to append to end of ``img_path`` basename for
registered image files that were output to a modified name;
defaults to None.
labels_img_sitk: Labels image as a SimpleITK ``Image`` object;
defaults to None, in which case the registered labels image file
corresponding to ``img_path`` with any ``suffix`` modifier
will be opened.
Returns:
Tuple of the density image as a Numpy array in the same shape as
the opened image; Numpy array of blob IDs; and the original
``img_path`` to track such as for multiprocessing.
"""
mod_path = img_path
if suffix is not None:
mod_path = libmag.insert_before_ext(img_path, suffix)
if labels_img_sitk is None:
labels_img_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_img = sitk.GetArrayFromImage(labels_img_sitk)
# load blobs
blobs, scaling, _ = np_io.load_blobs(img_path, True, labels_img.shape,
scale)
if shape is not None:
# scale blob coordinates and heat map to an alternative final shape
scaling = np.divide(shape, np.divide(labels_img.shape, scaling))
labels_spacing = np.multiply(labels_img_sitk.GetSpacing()[::-1],
np.divide(labels_img.shape, shape))
labels_img = np.zeros(shape, dtype=labels_img.dtype)
labels_img_sitk.SetSpacing(labels_spacing[::-1])
print("using scaling: {}".format(scaling))
# annotate blobs based on position
blobs_ids, coord_scaled = ontology.get_label_ids_from_position(
blobs[:, :3], labels_img, scaling, return_coord_scaled=True)
print("blobs_ids: {}".format(blobs_ids))
# build heat map to store densities per label px and save to file
heat_map = cv_nd.build_heat_map(labels_img.shape, coord_scaled)
out_path = sitk_io.reg_out_path(mod_path,
config.RegNames.IMG_HEAT_MAP.value)
print("writing {}".format(out_path))
heat_map_sitk = sitk_io.replace_sitk_with_numpy(labels_img_sitk, heat_map)
sitk.WriteImage(heat_map_sitk, out_path, False)
return heat_map, blobs_ids, img_path
def edge_aware_segmentation(
path_atlas: str, atlas_profile: atlas_prof.AtlasProfile,
show: bool = True, atlas: bool = True, suffix: Optional[str] = None,
exclude_labels: Optional[pd.DataFrame] = None, mirror_mult: int = -1):
"""Segment an atlas using its previously generated edge map.
Labels may not match their own underlying atlas image well,
particularly in the orthogonal directions in which the labels
were not constructed. To improve alignment between the labels
and the atlas itself, register the labels to an automated, roughly
segmented version of the atlas. The goal is to improve the
labels' alignment so that the atlas/labels combination can be
used for another form of automated segmentation by registering
them to experimental brains via :func:``register``.
Edge files are assumed to have been generated by
:func:``make_edge_images``.
Args:
path_atlas: Path to the fixed file, typically the atlas file
with stained sections. The corresponding edge and labels
files will be loaded based on this path.
atlas_profile: Atlas profile.
show: True if the output images should be displayed; defaults
to True.
atlas: True if the primary image is an atlas, which is assumed
to be symmetrical. False if the image is an experimental/sample
image, in which case segmentation will be performed on the full
images, and stats will not be performed.
suffix: Modifier to append to end of ``path_atlas`` basename for
registered image files that were output to a modified name;
defaults to None. If ``atlas`` is True, ``suffix`` will only
be applied to saved files, with files still loaded based on the
original path.
exclude_labels: Sequence of labels to exclude from the
segmentation; defaults to None.
mirror_mult: Multiplier for mirrored labels; defaults to -1
to make mirrored labels the inverse of their source labels.
"""
# adjust image path with suffix
load_path = path_atlas
mod_path = path_atlas
if suffix is not None:
mod_path = libmag.insert_before_ext(mod_path, suffix)
if atlas: load_path = mod_path
# load corresponding files via SimpleITK
atlas_sitk = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_ATLAS.value, get_sitk=True)
atlas_sitk_edge = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_ATLAS_EDGE.value, get_sitk=True)
labels_sitk = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_sitk_markers = sitk_io.load_registered_img(
load_path, config.RegNames.IMG_LABELS_MARKERS.value, get_sitk=True)
# get Numpy arrays of images
atlas_img_np = sitk.GetArrayFromImage(atlas_sitk)
atlas_edge = sitk.GetArrayFromImage(atlas_sitk_edge)
labels_img_np = sitk.GetArrayFromImage(labels_sitk)
markers = sitk.GetArrayFromImage(labels_sitk_markers)
# segment image from markers
sym_axis = atlas_refiner.find_symmetric_axis(atlas_img_np)
mirrorred = atlas and sym_axis >= 0
len_half = None
seg_args = {"exclude_labels": exclude_labels}
edge_prof = atlas_profile[profiles.RegKeys.EDGE_AWARE_REANNOTATION]
if edge_prof:
edge_filt = edge_prof[profiles.RegKeys.WATERSHED_MASK_FILTER]
if edge_filt and len(edge_filt) > 1:
# watershed mask filter settings from atlas profile
seg_args["mask_filt"] = edge_filt[0]
seg_args["mask_filt_size"] = edge_filt[1]
if mirrorred:
# segment only half of image, assuming symmetry
len_half = atlas_img_np.shape[sym_axis] // 2
slices = [slice(None)] * labels_img_np.ndim
slices[sym_axis] = slice(len_half)
sl = tuple(slices)
labels_seg = segmenter.segment_from_labels(
atlas_edge[sl], markers[sl], labels_img_np[sl], **seg_args)
else:
# segment the full image, including excluded labels on the opposite side
exclude_labels = exclude_labels.tolist().extend(
(mirror_mult * exclude_labels).tolist())
seg_args["exclude_labels"] = exclude_labels
labels_seg = segmenter.segment_from_labels(
atlas_edge, markers, labels_img_np, **seg_args)
smoothing = atlas_profile["smooth"]
smoothing_mode = atlas_profile["smoothing_mode"]
cond = ["edge-aware_seg"]
if smoothing is not None:
# smoothing by opening operation based on profile setting
meas_smoothing = atlas_profile["meas_smoothing"]
cond.append("smoothing")
df_aggr, df_raw = atlas_refiner.smooth_labels(
labels_seg, smoothing, smoothing_mode,
meas_smoothing, labels_sitk.GetSpacing()[::-1])
df_base_path = os.path.splitext(mod_path)[0]
if df_raw is not None:
# write raw smoothing metrics
df_io.data_frames_to_csv(
df_raw, f"{df_base_path}_{config.PATH_SMOOTHING_RAW_METRICS}")
if df_aggr is not None:
# write aggregated smoothing metrics
df_io.data_frames_to_csv(
df_aggr, f"{df_base_path}_{config.PATH_SMOOTHING_METRICS}")
if mirrorred:
# mirror back to other half
labels_seg = _mirror_imported_labels(
labels_seg, len_half, mirror_mult, sym_axis)
# expand background to smoothed background of original labels to
# roughly match background while still allowing holes to be filled
crop = atlas_profile["crop_to_orig"]
atlas_refiner.crop_to_orig(
labels_img_np, labels_seg, crop)
if labels_seg.dtype != labels_img_np.dtype:
# watershed may give different output type, so cast back if so
labels_seg = labels_seg.astype(labels_img_np.dtype)
labels_sitk_seg = sitk_io.replace_sitk_with_numpy(labels_sitk, labels_seg)
# show DSCs for labels
_logger.info(
"\nMeasuring overlap of individual original and watershed labels:")
dsc_lbls_comb = atlas_refiner.measure_overlap_labels(
labels_sitk, labels_sitk_seg)
_logger.info(
"\nMeasuring overlap of combined original and watershed labels:")
dsc_lbls_indiv = atlas_refiner.measure_overlap_labels(
atlas_refiner.make_labels_fg(labels_sitk),
atlas_refiner.make_labels_fg(labels_sitk_seg))
_logger.info("")
# measure and save whole atlas metrics
metrics = {
config.AtlasMetrics.SAMPLE: [os.path.basename(mod_path)],
config.AtlasMetrics.REGION: config.REGION_ALL,
config.AtlasMetrics.CONDITION: "|".join(cond),
config.AtlasMetrics.DSC_LABELS_ORIG_NEW_COMBINED: dsc_lbls_comb,
config.AtlasMetrics.DSC_LABELS_ORIG_NEW_INDIV: dsc_lbls_indiv,
}
df_metrics_path = libmag.combine_paths(
mod_path, config.PATH_ATLAS_IMPORT_METRICS)
atlas_refiner.measure_atlas_refinement(
metrics, atlas_sitk, labels_sitk_seg, atlas_profile, df_metrics_path)
# show and write image to same directory as atlas with appropriate suffix
sitk_io.write_reg_images(
{config.RegNames.IMG_LABELS.value: labels_sitk_seg}, mod_path)
if show: sitk.Show(labels_sitk_seg)
return path_atlas
def make_density_image(img_path, scale=None, shape=None, suffix=None,
labels_img_sitk=None, channel=None, matches=None):
"""Make a density image based on associated blobs.
Uses the shape of the registered labels image by default to set
the voxel sizes for the blobs.
If ``matches`` is given, a heat map will be generated for each set
of channels given in the dictionary. Otherwise, if the loaded blobs
file has intensity-based colocalizations, a heat map will be generated
for each combination of channels.
Args:
img_path: Path to image, which will be used to indentify the blobs file.
scale: Rescaling factor as a scalar value to find the corresponding
full-sized image. Defaults to None to use the register
setting ``target_size`` instead if available, falling back
to load the full size image to find its shape if necessary.
shape: Final shape size; defaults to None to use the shape of
the labels image.
suffix: Modifier to append to end of ``img_path`` basename for
registered image files that were output to a modified name;
defaults to None.
labels_img_sitk: Labels image as a SimpleITK ``Image`` object;
defaults to None, in which case the registered labels image file
corresponding to ``img_path`` with any ``suffix`` modifier
will be opened.
channel (List[int]): Sequence of channels to include in density image;
defaults to None to combine blobs from all channels.
matches (dict[tuple[int, int], :class:`magmap.cv.colocalizer`):
Dictionary of channel combinations to blob matches; defaults to
None.
Returns:
:obj:`np.ndarray`, str: The density image as a Numpy array in the
same shape as the opened image and the original and ``img_path``
to track such as for multiprocessing.
"""
def make_heat_map():
# build heat map to store densities per label px and save to file
coord_scaled = ontology.scale_coords(
blobs_chl[:, :3], scaling, labels_img.shape)
print("coords", coord_scaled)
return cv_nd.build_heat_map(labels_img.shape, coord_scaled)
# set up paths and get labels image
mod_path = img_path
if suffix is not None:
mod_path = libmag.insert_before_ext(img_path, suffix)
if labels_img_sitk is None:
labels_img_sitk = sitk_io.load_registered_img(
mod_path, config.RegNames.IMG_LABELS.value, get_sitk=True)
labels_img = sitk.GetArrayFromImage(labels_img_sitk)
# load blobs
blobs = detector.Blobs().load_blobs(np_io.img_to_blobs_path(img_path))
scaling = np_io.find_scaling(img_path, labels_img.shape, scale)[0]
if shape is not None:
# scale blob coordinates and heat map to an alternative final shape
scaling = np.divide(shape, np.divide(labels_img.shape, scaling))
labels_spacing = np.multiply(
labels_img_sitk.GetSpacing()[::-1],
np.divide(labels_img.shape, shape))
labels_img = np.zeros(shape, dtype=labels_img.dtype)
labels_img_sitk.SetSpacing(labels_spacing[::-1])
print("using scaling: {}".format(scaling))
# annotate blobs based on position
blobs_chl = blobs.blobs
if channel is not None:
blobs_chl = blobs_chl[np.isin(detector.get_blobs_channel(
blobs_chl), channel)]
heat_map = make_heat_map()
print("heat map", heat_map.shape, heat_map.dtype, labels_img.shape)
imgs_write = {
config.RegNames.IMG_HEAT_MAP.value:
sitk_io.replace_sitk_with_numpy(labels_img_sitk, heat_map)}
heat_colocs = None
if matches:
# create heat maps for match-based colocalization combos
heat_colocs = []
for chl_combo, chl_matches in matches.items():
print("Generating match-based colocalization heat map "
"for channel combo:", chl_combo)
# use blobs in first channel of each channel pair for simplicity
blobs_chl = chl_matches.get_blobs(1)
heat_colocs.append(make_heat_map())
elif blobs.colocalizations is not None:
# create heat map for each intensity-based colocalization combo
# as a separate channel in output image
blob_chls = range(blobs.colocalizations.shape[1])
blob_chls_len = len(blob_chls)
if blob_chls_len > 1:
# get all channel combos that include given channels
combos = []
chls = blob_chls if channel is None else channel
for r in range(2, blob_chls_len + 1):
combos.extend(
[tuple(c) for c in itertools.combinations(blob_chls, r)
if all([h in c for h in chls])])
heat_colocs = []
for combo in combos:
print("Generating intensity-based colocalization heat map "
"for channel combo:", combo)
blobs_chl = blobs.blobs[np.all(np.equal(
blobs.colocalizations[:, combo], 1), axis=1)]
heat_colocs.append(make_heat_map())
if heat_colocs is not None:
# combine heat maps into single image
heat_colocs = np.stack(heat_colocs, axis=3)
imgs_write[config.RegNames.IMG_HEAT_COLOC.value] = \
sitk_io.replace_sitk_with_numpy(
labels_img_sitk, heat_colocs)
# write images to file
sitk_io.write_reg_images(imgs_write, mod_path)
return heat_map, img_path