MPP1.6 - Run LaminB1 full workflow

[Aerendel]

Story As a stakeholder, I want to see a complex workflow running in Napari so that I can identify any potential issues

A/C:

• I can run a full Lamin B1 interphase workflow on an image loaded in Napari

Details:

• Similar to MPP1.5 which has a full workflow for SEC61B - implement the same for LaminB1 interphase.
• This can be done in a new branch to make things easier and just replace SEC61B to avoid having to create extra screens

[jxchen01]

We can try lamin b1 interphase workflow. It is one of the complicated ones.

https://github.com/AllenCell/aics-segmentation/blob/feature/json_workflow/aicssegmentation/structure_wrapper_config/conf_lmnb1_interphase.json

[jxchen01]

here is the code:

from aicssegmentation.core.pre_processing_utils import  intensity_normalization, image_smoothing_gaussian_3d
from aicssegmentation.core.vessel import  vesselnessSliceBySlice
from aicssegmentation.core.utils import segmentation_union, get_seed_for_objects, remove_index_object
from skimage.morphology import watershed
from skimage.segmentation import find_boundaries

#step 1
scaling_param = [4000]
img_1 = intensity_normalization(img_0, scaling_param = scaling_param)

#step2
sigma = 1
img_2 = image_smoothing_gaussian_3d(img_1, sigma = sigma)

#step3
sigmas = [1]
cutoff = 0.01
img_3 = vesselnessSliceBySlice(img_2, sigmas = sigmas, cutoff = cutoff)

#step4
sigmas = [2]
cutoff = 0.01
img_4 = vesselnessSliceBySlice(img_2, sigmas = sigmas, cutoff = cutoff)

#step5
sigmas = [3]
cutoff = 0.01
img_5 = vesselnessSliceBySlice(img_2, sigmas = sigmas, cutoff = cutoff)

#step6
img_6 = segmentation_union([img_3, img_4, img_5])

#step7
area_min = 1
area_max = 10
bg_seed = True
img_7 = get_seed_for_objects(img_2, img_6, area_min = area_min, area_max = area_max, bg_seed = bg_seed)

#step8
watershed_line = True
img_8 = skimage.morphology(img_1, img_7, watershed_line = watershed_line)

#step9
id_to_remove = [1]
img_9 = remove_index_object(img_8, id_to_remove=id_to_remove)

#step10
connectivity = 1
method = "thick"
img_10 = find_boundaries(img_9, connectivity = connectivity, method = method) 

Here, img_10 will be the final result