import numpy as np
from typing import Union
from pathlib import Path
from aicssegmentation.core.vessel import vesselness3D
from aicssegmentation.core.seg_dot import dot_3d
from aicssegmentation.core.pre_processing_utils import (
intensity_normalization,
edge_preserving_smoothing_3d,
)
from skimage.morphology import remove_small_objects
from aicssegmentation.core.output_utils import (
save_segmentation,
generate_segmentation_contour,
)
from skimage.filters import threshold_otsu
[docs]def Workflow_PCNA_earlyS_midS(
struct_img: np.ndarray,
rescale_ratio: float = -1,
output_type: str = "default",
output_path: Union[str, Path] = None,
fn: Union[str, Path] = None,
output_func=None,
):
"""
classic segmentation workflow wrapper for structure pcna
Parameter:
-----------
struct_img: np.ndarray
the 3D image to be segmented
rescale_ratio: float
an optional parameter to allow rescale the image before running the
segmentation functions, default is no rescaling
output_type: str
select how to handle output. Currently, four types are supported:
1. default: the result will be saved at output_path whose filename is
original name without extention + "_struct_segmentaiton.tiff"
2. array: the segmentation result will be simply returned as a numpy array
3. array_with_contour: segmentation result will be returned together with
the contour of the segmentation
4. customize: pass in an extra output_func to do a special save. All the
intermediate results, names of these results, the output_path, and the
original filename (without extension) will be passed in to output_func.
"""
##########################################################################
# PARAMETERS:
# note that these parameters are supposed to be fixed for the structure
# and work well accross different datasets
##########################################################################
intensity_norm_param = [0.5, 10]
intensity_norm_param_otsu = [0.5, 15]
scaling_factor = 1.0
vesselness_sigma = [1.0]
vesselness_cutoff = 0.0125
minArea = 20
dot_3d_sigma = 1.0
##########################################################################
out_img_list = []
out_name_list = []
###################
# PRE_PROCESSING
###################
# intenisty normalization (min/max)
struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
struct_img_otsu = intensity_normalization(struct_img, scaling_param=intensity_norm_param_otsu)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
# smoothing with boundary preserving smoothing
structure_img_smooth = edge_preserving_smoothing_3d(struct_img)
structure_img_smooth_otsu = edge_preserving_smoothing_3d(struct_img_otsu)
out_img_list.append(structure_img_smooth.copy())
out_name_list.append("im_smooth")
out_img_list.append(structure_img_smooth_otsu.copy())
out_name_list.append("im_smooth_otsu")
###################
# core algorithm
###################
otsu_thresh = threshold_otsu(structure_img_smooth_otsu)
global_thresh = otsu_thresh * scaling_factor
bw_otsu_mask = structure_img_smooth_otsu > global_thresh
response_f3 = vesselness3D(structure_img_smooth, sigmas=vesselness_sigma, tau=1, whiteonblack=True)
response_f3 = response_f3 > vesselness_cutoff
response_s3_1 = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
response_s3_3 = dot_3d(structure_img_smooth, log_sigma=3)
bw_small_inverse = remove_small_objects(response_s3_1 > 0.035, min_size=150)
bw_small = np.logical_xor(bw_small_inverse, response_s3_1 > 0.025)
bw_medium = np.logical_or(bw_small, response_s3_1 > 0.07)
bw_large = np.logical_or(response_s3_3 > 0.2, response_f3 > 0.25)
bw_dots = np.logical_or(np.logical_or(bw_small, bw_medium), bw_large)
bw = np.logical_and(bw_dots, bw_otsu_mask)
###################
# POST-PROCESSING
###################
bw = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
for zz in range(bw.shape[0]):
bw[zz, :, :] = remove_small_objects(bw[zz, :, :], min_size=3, connectivity=1, in_place=False)
seg = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
from aicssegmentation.core.utils import remove_hot_pixel
seg = seg > 0
seg_clean = remove_hot_pixel(seg)
seg = seg_clean.astype(np.uint8)
seg[seg > 0] = 255
out_img_list.append(seg.copy())
out_name_list.append("bw_final")
if output_type == "default":
# the default final output, simply save it to the output path
save_segmentation(seg, False, Path(output_path), fn)
elif output_type == "customize":
# the hook for passing in a customized output function
# use "out_img_list" and "out_name_list" in your hook to
# customize your output functions
output_func(out_img_list, out_name_list, Path(output_path), fn)
elif output_type == "array":
return seg
elif output_type == "array_with_contour":
return (seg, generate_segmentation_contour(seg))
else:
raise NotImplementedError("invalid output type: {output_type}")