def __init__(self, primary_images: ImageStack, nuclei: ImageStack) -> None:
"""Implements watershed segmentation of cells seeded from a nuclei image
Algorithm is seeded by a nuclei image. Binary segmentation mask is computed from a maximum
projection of spots across C and R, which is subsequently thresholded.
Parameters
----------
primary_images : ImageStack
primary hybridization images
nuclei : ImageStack
nuclei image
"""
# create a 'stain' for segmentation
mp = primary_images.reduce({Axes.CH, Axes.ZPLANE}, func="max")
self.stain = mp.reduce({Axes.ROUND},
func="mean",
level_method=Levels.SCALE_BY_IMAGE)
self.nuclei_mp_scaled = nuclei.reduce(
{Axes.ROUND, Axes.CH, Axes.ZPLANE},
func="max",
level_method=Levels.SCALE_BY_IMAGE,
)
self.markers: Optional[BinaryMaskCollection] = None
self.num_cells: Optional[int] = None
self.mask: Optional[BinaryMaskCollection] = None
self.segmented: Optional[BinaryMaskCollection] = None
def run(self, primary_images: ImageStack, nuclei: ImageStack,
*args) -> BinaryMaskCollection:
"""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 : BinaryMaskCollection
binary masks segmenting each cell
"""
# create a 'stain' for segmentation
mp = primary_images.reduce({Axes.CH, Axes.ZPLANE}, func="max")
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.reduce({Axes.ROUND, Axes.CH, Axes.ZPLANE},
func="max")
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 BinaryMaskCollection.from_label_image(label_image,
physical_ticks)
