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Copy pathcellpose_segmentation.py
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157 lines (130 loc) · 6.04 KB
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import cv2
import numpy as np
from skimage.exposure import rescale_intensity
from skimage.filters import gaussian
from skimage.io import imsave
from scipy import ndimage as ndi
from skimage.segmentation import watershed, relabel_sequential
def sharpen(image):
pl, ph = np.percentile(image, (0.1, 99.9))
img_rescale = rescale_intensity(image, in_range=(pl, ph))
return img_rescale
def adaptive_hist(image):
q10 = np.percentile(image, 10.0)
image[image < q10] = q10
return image
def assign_cyto_region(cyto_mask, seg_nuclei, result, fill_only_unfilled=False):
target = cyto_mask if not fill_only_unfilled else (cyto_mask & (result == 0))
if not target.any():
return
seeds = np.where(cyto_mask, seg_nuclei, 0)
nuc_ids = np.unique(seeds)
nuc_ids = nuc_ids[nuc_ids != 0]
if len(nuc_ids) == 0:
dilated_labeled = ndi.grey_dilation(seg_nuclei, size=3)
seeds = np.where(cyto_mask, dilated_labeled, 0)
nuc_ids = np.unique(seeds)
nuc_ids = nuc_ids[nuc_ids != 0]
if len(nuc_ids) == 0:
return
if len(nuc_ids) == 1:
result[target] = nuc_ids[0]
else:
dist = ndi.distance_transform_edt(cyto_mask)
wl = watershed(-dist, seeds, mask=cyto_mask)
if fill_only_unfilled:
result[target] = wl[target]
else:
result[cyto_mask] = wl[cyto_mask]
def merge_segmentations(seg_nuclei, seg_cyto1, seg_cyto2, nuc_diameter):
height, width = seg_nuclei.shape
result = np.zeros((height, width), dtype=np.int32)
unique_nucs = sorted(n for n in np.unique(seg_nuclei) if n != 0)
if seg_cyto1 is not None:
nuc_primary_cyto1 = {}
for nuc in unique_nucs:
nuc_pixels = seg_cyto1[seg_nuclei == nuc]
vals, cnts = np.unique(nuc_pixels, return_counts=True)
nz = vals != 0
if nz.any():
nuc_primary_cyto1[nuc] = vals[nz][np.argmax(cnts[nz])]
for c1 in set(nuc_primary_cyto1.values()):
assign_cyto_region(seg_cyto1 == c1, seg_nuclei, result, fill_only_unfilled=False)
if seg_cyto2 is not None:
nuc_primary_cyto2 = {}
for nuc in unique_nucs:
nuc_pixels = seg_cyto2[seg_nuclei == nuc]
vals, cnts = np.unique(nuc_pixels, return_counts=True)
nz = vals != 0
if nz.any():
nuc_primary_cyto2[nuc] = vals[nz][np.argmax(cnts[nz])]
for c2 in set(nuc_primary_cyto2.values()):
assign_cyto_region(seg_cyto2 == c2, seg_nuclei, result, fill_only_unfilled=True)
for nuc in unique_nucs:
result[seg_nuclei == nuc] = nuc
return relabel_sequential(result)[0]
def segment(model_nuc, model_cyto, nuclei_img, cyto_img1, cyto_img2, nuc_diameter, cell_diameter, output_folder,
output_prefix, model_cpsam=None, use_cpsam=False, normalize_nuclei=False):
channels = [1, 0]
if normalize_nuclei:
blurred = gaussian(nuclei_img, sigma=1.0, preserve_range=True)
white_point = np.percentile(blurred, 99)
nuc_input = np.power(
rescale_intensity(blurred, in_range=(0.0, white_point), out_range=(0.0, 1.0)),
0.35,
).astype(np.float32)
cv2.imwrite(output_folder + "/" + output_prefix + "nuclei_normalized.jpg",
(nuc_input * 255).astype(np.uint8),
[cv2.IMWRITE_JPEG_QUALITY, 75])
else:
nuc_input = nuclei_img
nuclei_masks, flows, styles = model_nuc.eval(
np.stack([nuc_input, np.zeros_like(nuc_input)]),
channels=channels,
diameter=nuc_diameter,
normalize=not normalize_nuclei,
)
imsave(output_folder + "/" + output_prefix + "nuclei_mask.png", nuclei_masks, check_contrast=False)
if cyto_img1 is not None:
cell_masks = None
if use_cpsam and model_cpsam is not None:
# Single CellposeSAM forward pass on all channels stacked as (H, W, 3).
# The model sees nuclear + cyto1 + cyto2 simultaneously, so no secondary
# channel is needed in the merge step.
ch0 = sharpen(adaptive_hist(nuclei_img)).astype(np.float32)
ch1 = sharpen(adaptive_hist(cyto_img1)).astype(np.float32)
ch2 = sharpen(adaptive_hist(cyto_img2)).astype(np.float32) if cyto_img2 is not None \
else np.zeros_like(ch0, dtype=np.float32)
rgb = np.stack([ch0, ch1, ch2], axis=-1)
sam_masks, flows, styles = model_cpsam.eval(
rgb,
diameter=cell_diameter,
flow_threshold=0.8,
cellprob_threshold=-0.4,
normalize=False,
)
imsave(output_folder + "/" + output_prefix + "sam_raw_mask.png", sam_masks, check_contrast=False)
cell_masks = merge_segmentations(nuclei_masks, sam_masks, None, nuc_diameter)
else:
channels = [1, 2]
cyto1_masks, flows, styles = model_cyto.eval(
np.stack([sharpen(adaptive_hist(cyto_img1)), nuclei_masks]),
channels=channels,
diameter=cell_diameter,
flow_threshold=0.8,
cellprob_threshold=-0.4
)
imsave(output_folder + "/" + output_prefix + "cyto1_mask.png", cyto1_masks, check_contrast=False)
if cyto_img2 is not None:
cyto2_masks, flows, styles = model_cyto.eval(
np.stack([sharpen(adaptive_hist(cyto_img2)), nuclei_masks]),
channels=channels,
diameter=cell_diameter,
flow_threshold=0.8,
cellprob_threshold=-0.4
)
imsave(output_folder + "/" + output_prefix + "cyto2_mask.png", cyto2_masks, check_contrast=False)
cell_masks = merge_segmentations(nuclei_masks, cyto1_masks, cyto2_masks, nuc_diameter)
else:
cell_masks = merge_segmentations(nuclei_masks, cyto1_masks, None, nuc_diameter)
imsave(output_folder + "/" + output_prefix + "cell_mask.png", cell_masks, check_contrast=False)