def segment_images(inpDir, outDir, config_data):
""" Workflow for data with similar morphology
as sialyltransferase 1.
Args:
inpDir : path to the input directory
outDir : path to the output directory
config_data : path to the configuration file
"""
logging.basicConfig(format='%(asctime)s - %(name)-8s - %(levelname)-8s - %(message)s',
datefmt='%d-%b-%y %H:%M:%S')
logger = logging.getLogger("main")
logger.setLevel(logging.INFO)
inpDir_files = os.listdir(inpDir)
for i,f in enumerate(inpDir_files):
logger.info('Segmenting image : {}'.format(f))
# Load image
br = BioReader(os.path.join(inpDir,f))
image = br.read_image()
structure_channel = 0
struct_img0 = image[:,:,:,structure_channel,0]
struct_img0 = struct_img0.transpose(2,0,1).astype(np.float32)
# main algorithm
intensity_scaling_param = config_data['intensity_scaling_param']
struct_img = intensity_normalization(struct_img0, scaling_param=intensity_scaling_param)
gaussian_smoothing_sigma = config_data['gaussian_smoothing_sigma']
structure_img_smooth = image_smoothing_gaussian_3d(struct_img, sigma=gaussian_smoothing_sigma)
global_thresh_method = config_data['global_thresh_method']
object_minArea = config_data['object_minArea']
bw, object_for_debug = MO(structure_img_smooth, global_thresh_method=global_thresh_method, object_minArea=object_minArea, return_object=True)
thin_dist_preserve = config_data['thin_dist_preserve']
thin_dist = config_data['thin_dist']
bw_thin = topology_preserving_thinning(bw>0, thin_dist_preserve, thin_dist)
s3_param = config_data['s3_param']
bw_extra = dot_3d_wrapper(structure_img_smooth, s3_param)
bw_combine = np.logical_or(bw_extra>0, bw_thin)
minArea = config_data['minArea']
seg = remove_small_objects(bw_combine>0, min_size=minArea, connectivity=1, in_place=False)
seg = seg > 0
out_img=seg.astype(np.uint8)
out_img[out_img>0]=255
# create output image
out_img = out_img.transpose(1,2,0)
out_img = out_img.reshape((out_img.shape[0], out_img.shape[1], out_img.shape[2], 1, 1))
# write image using BFIO
bw = BioWriter(os.path.join(outDir,f), metadata=br.read_metadata())
bw.num_x(out_img.shape[1])
bw.num_y(out_img.shape[0])
bw.num_z(out_img.shape[2])
bw.num_c(out_img.shape[3])
bw.num_t(out_img.shape[4])
bw.pixel_type(dtype='uint8')
bw.write_image(out_img)
bw.close_image()
def ST6GAL1_HiPSC_Pipeline(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 = [9, 19]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
cell_wise_min_area = 1200
dot_3d_sigma = 1.6
dot_3d_cutoff = 0.02
minArea = 10
thin_dist = 1
thin_dist_preserve = 1.6
##########################################################################
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 = processing.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
###################
# cell-wise local adaptive thresholding
th_low_level = threshold_triangle(structure_img_smooth)
bw_low_level = structure_img_smooth > th_low_level
bw_low_level = remove_small_objects(bw_low_level,
min_size=cell_wise_min_area,
connectivity=1,
in_place=True)
bw_low_level = dilation(bw_low_level, selem=ball(2))
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 > 0])
bw_high_level[np.logical_and(structure_img_smooth > local_otsu * 0.98,
single_obj)] = 1
# LOG 3d to capture spots
response = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
bw_extra = response > dot_3d_cutoff
# thinning
bw_high_level = topology_preserving_thinning(bw_high_level,
thin_dist_preserve, thin_dist)
# combine the two parts
bw = np.logical_or(bw_high_level, bw_extra)
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw > 0,
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
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)
else:
# the hook for other pre-defined RnD output functions (AICS internal)
ST6GAL1_output(out_img_list, out_name_list, output_type, output_path,
fn)
def Workflow_st6gal1(
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 ST6GAL1
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 = [9, 19]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
cell_wise_min_area = 1200
dot_3d_sigma = 1.6
dot_3d_cutoff = 0.02
minArea = 10
thin_dist = 1
thin_dist_preserve = 1.6
##########################################################################
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
###################
# cell-wise local adaptive thresholding
th_low_level = threshold_triangle(structure_img_smooth)
bw_low_level = structure_img_smooth > th_low_level
bw_low_level = remove_small_objects(
bw_low_level, min_size=cell_wise_min_area, connectivity=1, in_place=True
)
bw_low_level = dilation(bw_low_level, selem=ball(2))
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 > 0])
bw_high_level[
np.logical_and(structure_img_smooth > local_otsu * 0.98, single_obj)
] = 1
# LOG 3d to capture spots
response = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
bw_extra = response > dot_3d_cutoff
# thinning
bw_high_level = topology_preserving_thinning(
bw_high_level, thin_dist_preserve, thin_dist
)
# combine the two parts
bw = np.logical_or(bw_high_level, bw_extra)
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw > 0, 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}")