import numpy as np
from typing import Union
from pathlib import Path
from skimage.morphology import remove_small_objects
from aicssegmentation.core.pre_processing_utils import (
intensity_normalization,
image_smoothing_gaussian_3d,
)
from aicssegmentation.core.seg_dot import dot_slice_by_slice
from skimage.filters import threshold_triangle, threshold_otsu
from skimage.measure import label
from aicssegmentation.core.output_utils import (
save_segmentation,
generate_segmentation_contour,
)
from scipy.ndimage import zoom
[docs]def Workflow_cardio_fbl(
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 Cardio FBL
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, 140]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
dot_2d_sigma = 1
dot_2d_cutoff = 0.015
minArea = 1
low_level_min_size = 1000
##########################################################################
out_img_list = []
out_name_list = []
###################
# PRE_PROCESSING
###################
# intenisty normalization (min/max)
struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
# rescale if needed
if rescale_ratio > 0:
struct_img = zoom(struct_img, (1, rescale_ratio, rescale_ratio), order=2)
struct_img = (struct_img - struct_img.min() + 1e-8) / (struct_img.max() - struct_img.min() + 1e-8)
gaussian_smoothing_truncate_range = gaussian_smoothing_truncate_range * rescale_ratio
# smoothing with gaussian filter
structure_img_smooth = image_smoothing_gaussian_3d(
struct_img,
sigma=gaussian_smoothing_sigma,
truncate_range=gaussian_smoothing_truncate_range,
)
out_img_list.append(structure_img_smooth.copy())
out_name_list.append("im_smooth")
###################
# core algorithm
###################
# step 1: low level thresholding
# global_otsu = threshold_otsu(structure_img_smooth)
global_tri = threshold_triangle(structure_img_smooth)
global_median = np.percentile(structure_img_smooth, 50)
th_low_level = (global_tri + global_median) / 2
bw_low_level = structure_img_smooth > th_low_level
bw_low_level = remove_small_objects(bw_low_level, min_size=low_level_min_size, connectivity=1, out=bw_low_level)
# step 2: high level thresholding
local_cutoff = 0.333 * threshold_otsu(structure_img_smooth)
bw_high_level = np.zeros_like(bw_low_level)
lab_low, num_obj = label(bw_low_level, return_num=True, connectivity=1)
for idx in range(num_obj):
single_obj = lab_low == (idx + 1)
local_otsu = threshold_otsu(structure_img_smooth[single_obj])
if local_otsu > local_cutoff:
bw_high_level[np.logical_and(structure_img_smooth > local_otsu, single_obj)] = 1
out_img_list.append(bw_high_level.copy())
out_name_list.append("interm_high")
# step 3: finer segmentation
response2d = dot_slice_by_slice(structure_img_smooth, log_sigma=dot_2d_sigma)
bw_finer = remove_small_objects(response2d > dot_2d_cutoff, min_size=minArea, connectivity=1)
out_img_list.append(bw_finer.copy())
out_name_list.append("bw_fine")
# merge finer level detection into high level coarse segmentation to include
# outside dim parts
bw_high_level[bw_finer > 0] = 1
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw_high_level > 0, min_size=minArea, connectivity=1)
# output
seg = seg > 0
seg = seg.astype(np.uint8)
seg[seg > 0] = 255
out_img_list.append(seg.copy())
out_name_list.append("bw_coarse")
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}")