def Workflow_cardio_npm1_100x(
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 NPM1 100x
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, 2.5]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
dot_2d_sigma = 2
# dot_2d_sigma_extra = 1
# dot_2d_cutoff = 0.025
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
# print(global_median)
# print(global_tri)
# print(th_low_level)
# imsave('img_smooth.tiff', structure_img_smooth)
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,
in_place=True)
bw_low_level = dilation(bw_low_level, selem=ball(2))
# 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 > 1.2 * local_otsu, single_obj)] = 1
# imsave('seg_coarse.tiff', bw_high_level.astype(np.uint8))
out_img_list.append(bw_high_level.copy())
out_name_list.append("bw_coarse")
response_bright = dot_slice_by_slice(structure_img_smooth,
log_sigma=dot_2d_sigma)
bw_extra = response_bright > 0.03 # dot_2d_cutoff
bw_extra[~bw_low_level] = 0
bw_final = np.logical_or(bw_extra, bw_high_level)
# bw_final[holes]=0
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw_final,
min_size=minArea,
connectivity=1,
in_place=True)
# output
seg = seg > 0
seg = seg.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}")
def Workflow_cardio_npm1_100x(struct_img,
rescale_ratio,
output_type,
output_path,
fn,
output_func=None):
##########################################################################
# PARAMETERS:
# note that these parameters are supposed to be fixed for the structure
# and work well accross different datasets
intensity_norm_param = [0.5, 2.5]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
dot_2d_sigma = 2
dot_2d_sigma_extra = 1
dot_2d_cutoff = 0.025
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 = resize(struct_img, [1, rescale_ratio, rescale_ratio],
method="cubic")
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
#print(global_median)
#print(global_tri)
#print(th_low_level)
#imsave('img_smooth.tiff', structure_img_smooth)
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,
in_place=True)
bw_low_level = dilation(bw_low_level, selem=ball(2))
# 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 > 1.2 * local_otsu, single_obj)] = 1
#imsave('seg_coarse.tiff', bw_high_level.astype(np.uint8))
out_img_list.append(bw_high_level.copy())
out_name_list.append('bw_coarse')
response_bright = dot_slice_by_slice(structure_img_smooth,
log_sigma=dot_2d_sigma)
'''
response_dark = dot_slice_by_slice(1 - structure_img_smooth, log_sigma=dot_2d_sigma)
response_dark_extra = dot_slice_by_slice(1 - structure_img_smooth, log_sigma=dot_2d_sigma_extra)
imsave('res_dark_1.tiff', response_dark)
imsave('res_dark_2.tiff', response_dark_extra)
#inner_mask = bw_high_level.copy()
#for zz in range(inner_mask.shape[0]):
# inner_mask[zz,:,:] = binary_fill_holes(inner_mask[zz,:,:])
holes = np.logical_or(response_dark>dot_2d_cutoff , response_dark_extra>dot_2d_cutoff)
#holes[~inner_mask] = 0
'''
bw_extra = response_bright > 0.03 # dot_2d_cutoff
bw_extra[~bw_low_level] = 0
bw_final = np.logical_or(bw_extra, bw_high_level)
#bw_final[holes]=0
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw_final,
min_size=minArea,
connectivity=1,
in_place=True)
# output
seg = seg > 0
seg = seg.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
save_segmentation(seg, False, output_path, fn)
elif output_type == 'AICS_pipeline':
# pre-defined output function for pipeline data
save_segmentation(seg, True, output_path, fn)
elif output_type == 'customize':
# the hook for passing in a customized output function
output_fun(out_img_list, out_name_list, output_path, fn)
elif output_type == 'array':
return seg
elif output_type == 'array_with_contour':
return (seg, generate_segmentation_contour(seg))
else:
# the hook for pre-defined RnD output functions (AICS internal)
img_list, name_list = NPM1_output(out_img_list, out_name_list,
output_type, output_path, fn)
if output_type == 'QCB':
return img_list, name_list
def Workflow_ctnnb1(
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 CTNNB1
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 = [4, 27]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
dot_2d_sigma = 1.5
dot_2d_cutoff = 0.01
minArea = 10
##########################################################################
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
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
###################
response = dot_slice_by_slice(structure_img_smooth, log_sigma=dot_2d_sigma)
bw = response > dot_2d_cutoff
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw,
min_size=minArea,
connectivity=1,
in_place=False)
# output
seg = seg > 0
seg = seg.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}")
def Workflow_lamp1(
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 LAMP1
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_scaling_param = [3, 19]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
minArea = 15
# ves_th_2d = 0.1
vesselness_sigma = [1]
vesselness_cutoff = 0.15
# hole_min = 60
hole_max = 1600
log_sigma_1 = 5
log_cutoff_1 = 0.09
log_sigma_2 = 2.5
log_cutoff_2 = 0.07
log_sigma_3 = 1
log_cutoff_3 = 0.01
##########################################################################
out_img_list = []
out_name_list = []
# intenisty normalization
struct_img = intensity_normalization(
struct_img, scaling_param=intensity_scaling_param
)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
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
)
structure_img_smooth = image_smoothing_gaussian_slice_by_slice(
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")
# spot detection
response1 = dot_slice_by_slice(structure_img_smooth, log_sigma=log_sigma_1)
bw1 = response1 > log_cutoff_1
response2 = dot_slice_by_slice(structure_img_smooth, log_sigma=log_sigma_2)
bw2 = response2 > log_cutoff_2
bw_spot = np.logical_or(bw1, bw2)
response3 = dot_slice_by_slice(structure_img_smooth, log_sigma=log_sigma_3)
bw3 = response3 > log_cutoff_3
bw_spot = np.logical_or(bw_spot, bw3)
# ring/filament detection
ves = vesselnessSliceBySlice(
structure_img_smooth, sigmas=vesselness_sigma, tau=1, whiteonblack=True
)
bw_ves = ves > vesselness_cutoff
# fill holes
partial_fill = np.logical_or(bw_spot, bw_ves)
out_img_list.append(partial_fill.copy())
out_name_list.append("interm_before_hole")
holes = np.zeros_like(partial_fill)
for zz in range(partial_fill.shape[0]):
background_lab = label(~partial_fill[zz, :, :], connectivity=1)
out = np.copy(background_lab)
component_sizes = np.bincount(background_lab.ravel())
too_big = component_sizes > hole_max
too_big_mask = too_big[background_lab]
out[too_big_mask] = 0
# too_small = component_sizes <hole_min
# too_small_mask = too_small[background_lab]
# out[too_small_mask] = 0
holes[zz, :, :] = out
full_fill = np.logical_or(partial_fill, holes)
seg = remove_small_objects(
full_fill, min_size=minArea, connectivity=1, in_place=False
)
# output
seg = seg > 0
seg = seg.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}")
