def _carve_segs(roi, blobs):
# carve out background from segmented area
carved = roi
if blobs is None:
# clean up by using simple threshold to remove all background
carved, _ = cv_nd.carve(carved)
else:
# use blobs as ellipsoids to identify background to remove;
# TODO: consider setting spacing in config since depends on
# microscopy characteristics, such as elongation from
# thick lightsheet
thresholded = plot_3d.build_ground_truth(
np.zeros(carved.shape, dtype=bool), blobs, ellipsoid=True)
#thresholded = thresholded.astype(bool)
carved[~thresholded] = 0
return carved
def segment_from_labels(edges,
markers,
labels_img,
atlas_img=None,
exclude_labels=None,
mask_filt=config.SmoothingModes.opening,
mask_filt_size=2):
"""Segment an image using markers from a labels image.
Labels images may have been generally manually and thus may not
perfectly match the underlying image. As a way to check or
augment the label image, segment the underlying image using
the labels as the seeds to prescribe the number and initial
location of each label.
Args:
edges (:obj:`np.ndarray`): Image as a Numpy array to segment,
typically an edge-detected image of the main atlas.
markers (:obj:`np.ndarray`): Image as an integer Numpy array of same
shape as ``img`` to use as seeds for the watershed segmentation.
This array is generally constructed from an array similar to
``labels_img``.
labels_img (:obj:`np.ndarray`): Labels image as Numpy array of same
shape as ``img``, used to generate a mask for the watershed.
If None, a mask will be generated from a thresholded version of
``atlas_img``, so should only be None if ``atlas_img`` is not None.
atlas_img (:obj:`np.ndarray`): Atlas image as a Numpy array to use
for finding foreground; defaults to None. If both ``labels_img``
and ``atlas_img`` are not None, their combined volume will be
used as a mask.
exclude_labels (List[int]): Sequence of labels to exclude from the
segmentation; defaults to None.
mask_filt (:obj:`config.SmoothingModes`): Enumeration for a filter
mode to use for the watershed mask; defaults to
:obj:`config.SmoothingModes.opening`. Ignored if ``atlas_img``
or both ``atlas_img`` and ``labels_img`` are given to generate
the mask.
mask_filt_size (int): Size of structuring element for the filter
specified by ``mask_filt``; defaults to 2.
Returns:
:obj:`np.ndarray`: Segmented image of the same shape as ``img`` with
the same number of labels as in ``markers``.
"""
# generate mask for watershed
if atlas_img is not None and labels_img is not None:
# broad mask from both atlas and labels
mask = mask_atlas(atlas_img, labels_img)
elif atlas_img is not None:
# otsu seems to give more inclusive threshold for these atlases
_, mask = cv_nd.carve(atlas_img,
thresh=filters.threshold_otsu(atlas_img),
holes_area=5000)
else:
# default to using label foreground
mask = labels_img != 0
fn_mask = None
if mask_filt is config.SmoothingModes.opening:
# default filter opens the mask to prevent spillover across
# artifacts that may bridge otherwise separate structures
fn_mask = morphology.binary_opening
elif mask_filt is config.SmoothingModes.closing:
fn_mask = morphology.binary_closing
if fn_mask and mask_filt_size:
print("Filtering watershed mask with {}, size {}".format(
fn_mask, mask_filt_size))
mask = fn_mask(mask,
cv_nd.get_selem(labels_img.ndim)(mask_filt_size))
exclude = None
if exclude_labels is not None:
# remove excluded labels from mask
exclude = np.isin(labels_img, exclude_labels)
mask[exclude] = False
# WORKAROUND: remove excluded markers from marker image itself for
# apparent Scikit-image bug (see PR 3809, fixed in 0.15)
markers[np.isin(markers, exclude_labels)] = 0
watershed = watershed_distance(edges == 0,
markers,
compactness=0.005,
mask=mask)
if exclude is not None:
# add excluded labels directly to watershed image
watershed[exclude] = labels_img[exclude]
return watershed
def segment_from_labels(edges, markers, labels_img, atlas_img=None,
exclude_labels=None):
"""Segment an image using markers from a labels image.
Labels images may have been generally manually and thus may not
perfectly match the underlying image. As a way to check or
augment the label image, segment the underlying image using
the labels as the seeds to prescribe the number and initial
location of each label.
Args:
edges (:obj:`np.ndarray`): Image as a Numpy array to segment,
typically an edge-detected image of the main atlas.
markers (:obj:`np.ndarray`): Image as an integer Numpy array of same
shape as ``img`` to use as seeds for the watershed segmentation.
This array is generally constructed from an array similar to
``labels_img``.
labels_img (:obj:`np.ndarray`): Labels image as Numpy array of same
shape as ``img``, used to generate a mask for the watershed.
If None, a mask will be generated from a thresholded version of
``atlas_img``, so should only be None if ``atlas_img`` is not None.
atlas_img (:obj:`np.ndarray`): Atlas image as a Numpy array to use
for finding foreground; defaults to None. If both ``labels_img``
and ``atlas_img`` are not None, their combined volume will be
used as a mask.
exclude_labels (List[int]): Sequence of labels to exclude from the
segmentation; defaults to None.
Returns:
:obj:`np.ndarray`: Segmented image of the same shape as ``img`` with
the same number of labels as in ``markers``.
"""
if atlas_img is not None and labels_img is not None:
# broad mask from both atlas and labels
mask = mask_atlas(atlas_img, labels_img)
elif atlas_img is not None:
# otsu seems to give more inclusive threshold for these atlases
_, mask = cv_nd.carve(
atlas_img, thresh=filters.threshold_otsu(atlas_img),
holes_area=5000)
else:
# default to using labels, opening them up small holes to prevent
# spillover across artifacts that may bridge them
fn_selem = cv_nd.get_selem(labels_img.ndim)
mask = morphology.binary_opening(labels_img != 0, fn_selem(2))
exclude = None
if exclude_labels is not None:
# remove excluded labels from mask
exclude = np.isin(labels_img, exclude_labels)
mask[exclude] = False
# WORKAROUND: remove excluded markers from marker image itself for
# apparent Scikit-image bug (see PR 3809, fixed in 0.15)
markers[np.isin(markers, exclude_labels)] = 0
watershed = watershed_distance(
edges == 0, markers, compactness=0.005, mask=mask)
if exclude is not None:
# add excluded labels directly to watershed image
watershed[exclude] = labels_img[exclude]
return watershed