def run(self, primary_images: ImageStack, nuclei: ImageStack,
*args) -> SegmentationMaskCollection:
"""Segments nuclei in 2-d using a nuclei ImageStack
Primary images are used to expand the nuclear mask, but only in cases where there are
densely detected points surrounding the nuclei.
Parameters
----------
primary_images : ImageStack
contains primary image data
nuclei : ImageStack
contains nuclei image data
Returns
-------
masks : SegmentationMaskCollection
binary masks segmenting each cell
"""
# create a 'stain' for segmentation
mp = primary_images.max_proj(Axes.CH, Axes.ZPLANE)
mp_numpy = mp._squeezed_numpy(Axes.CH, Axes.ZPLANE)
stain = np.mean(mp_numpy, axis=0)
stain = stain / stain.max()
# TODO make these parameterizable or determine whether they are useful or not
size_lim = (10, 10000)
disk_size_markers = None
disk_size_mask = None
nuclei_mp = nuclei.max_proj(Axes.ROUND, Axes.CH, Axes.ZPLANE)
nuclei__mp_numpy = nuclei_mp._squeezed_numpy(Axes.ROUND, Axes.CH,
Axes.ZPLANE)
self._segmentation_instance = _WatershedSegmenter(
nuclei__mp_numpy, stain)
label_image = self._segmentation_instance.segment(
self.nuclei_threshold, self.input_threshold, size_lim,
disk_size_markers, disk_size_mask, self.min_distance)
# we max-projected and squeezed the Z-plane so label_image.ndim == 2
physical_ticks = {
coord: nuclei.xarray.coords[coord.value].data
for coord in (Coordinates.Y, Coordinates.X)
}
return SegmentationMaskCollection.from_label_image(
label_image, physical_ticks)
def run(
self,
stack: ImageStack,
in_place: bool = False,
verbose: bool = False,
n_processes: Optional[int] = None,
*args,
) -> ImageStack:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
verbose : bool
if True, report on filtering progress (default = False)
n_processes : Optional[int]
Number of parallel processes to devote to calculating the filter
Returns
-------
ImageStack :
If in-place is False, return the results of filter as a new stack. Otherwise return the
original stack.
"""
return stack.max_proj(*tuple(Axes(dim) for dim in self.dims))