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Clean up watershed (again) #1693

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Dec 16, 2019
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Clean up watershed (again) #1693

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38 changes: 17 additions & 21 deletions starfish/core/image/Segment/watershed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
from skimage.feature import peak_local_max
from skimage.morphology import disk, watershed

from starfish.core.image.Filter import Reduce
from starfish.core.image.Filter.util import bin_open
from starfish.core.imagestack.imagestack import ImageStack
from starfish.core.morphology import Filter
Expand Down Expand Up @@ -77,21 +76,12 @@ def run(
masks : BinaryMaskCollection
binary masks segmenting each cell
"""

# create a 'stain' for segmentation
mp = primary_images.reduce({Axes.CH, Axes.ZPLANE}, func="max")
mean = Reduce(
dims=(Axes.ROUND,),
func="mean",
level_method=Levels.SCALE_BY_IMAGE).run(mp)
stain = mean._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)

# TODO make these parameterizable or determine whether they are useful or not
size_lim = (10, 10000)
disk_size_markers = None
disk_size_mask = None

self._segmentation_instance = _WatershedSegmenter(nuclei, stain)
self._segmentation_instance = _WatershedSegmenter(primary_images, nuclei)
label_image_array = self._segmentation_instance.segment(
self.nuclei_threshold, self.input_threshold, size_lim, disk_size_markers,
disk_size_mask, self.min_distance
Expand All @@ -118,28 +108,32 @@ def show(self, figsize: Tuple[int, int]=(10, 10)) -> None:


class _WatershedSegmenter:
def __init__(self, nuclei: ImageStack, stain_img: np.ndarray) -> None:
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
stain_img : np.ndarray[np.float32]
stain image
"""
max_project_and_scale = Reduce(
# 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.nuclei_mp_scaled = max_project_and_scale.run(nuclei)
self.stain = stain_img / stain_img.max()

self.nuclei_thresholded: Optional[np.ndarray] = None # dtype: bool
self.markers = None
self.num_cells: Optional[int] = None
self.mask = None
Expand Down Expand Up @@ -320,7 +314,7 @@ def watershed_mask(self, stain_thresh: Number, markers: np.ndarray, disk_size: O
thresholded stain image

"""
st = self.stain >= stain_thresh
st = self.stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE) >= stain_thresh
watershed_mask: np.ndarray = np.logical_or(st, markers > 0) # dtype bool
if disk_size is not None:
watershed_mask = bin_open(watershed_mask, disk_size)
Expand All @@ -342,7 +336,7 @@ def watershed(self, markers: np.ndarray, watershed_mask: np.ndarray) -> np.ndarr
np.ndarray[np.int32] :
labeled image, each segment has a unique integer value
"""
img = 1 - self.stain
img = 1 - self.stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)

res = watershed(image=img,
markers=markers,
Expand Down Expand Up @@ -384,7 +378,9 @@ def show(self, figsize=(10, 10)):
plt.title('Nuclei')

plt.subplot(322)
image(self.stain, ax=plt.gca(), size=20, bar=True)
image(
self.stain._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE),
ax=plt.gca(), size=20, bar=True)
plt.title('Stain')

plt.subplot(323)
Expand Down