More Image Processing Options: Cellpose and Denoising

Be sure to have gone through the main ImageProcessing notebook in PalmettoBUG before executing this one! This notebook depends on the directory being set up from there.

No other notebooks depend on this one – but outputs from this notebook (like cellpose masks in the /masks/cellpose folder) could be inserted into those other notebooks to replace corresponding default inputs in other notebooks (such as deepcell masks).

import os
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tifffile as tf

import isosegdenoise as iSD

CHANGE The following directory to match an existing directory on your computer if you are testing this tutorial on your own machine!

my_computer_path =  "C:/Users/Default/Desktop"

Load into isosegdenoise:

NOTE!: This notebook depends on the CORE_mcd_to_analysis notebook to have already been run / the example palmettobug project must already be setup in the project_directory! The isosegdenoise package cannot setup its own example data project (nor is the example data bundled with it), instead that must be done manually or through palmettobug.

## initialize denoise / esgmentation experiment:
project_directory = f"{my_computer_path}/Example_IMC"
resolutions = [1.0, 1.0]    ## in micrometers. 1 micron is standard IMC resolution
iSD_object = iSD.imc_entrypoint(directory = project_directory, resolutions = resolutions, from_mcds = True)

iSD_object.panel

	channel	name	keep	segmentation
0	I127	127I	0	NaN
1	Xe131	131Xe	0	NaN
2	Ba132	132Ba	0	NaN
3	Ba138	138Ba	0	NaN
4	Pr141	aSMA	1	NaN
5	Nd142	p-selectin	1	NaN
6	Nd143	Vimentin	1	NaN
7	Nd144	CD14	1	NaN
8	Nd145	CD31	1	NaN
9	Nd146	CD16	1	NaN
10	Nd148	Pan-Keratin	1	NaN
11	Sm149	CD11b	1	NaN
12	Eu151	CD163	1	NaN
13	Sm152	CD45	1	NaN
14	Eu153	CD206	1	NaN
15	Sm154	CCL2	1	NaN
16	Gd155	FoxP3	1	NaN
17	Gd156	CD4	1	NaN
18	Gd158	E-cadherin	1	NaN
19	Tb159	CD68	1	NaN
20	Gd160	CD66b	1	NaN
21	Dy161	CD20	1	NaN
22	Dy162	CD8	1	NaN
23	Dy163	Vitrionectin	1	NaN
24	Dy164	CD32b	1	NaN
25	Er167	GranzymeB	1	NaN
26	Er168	Ki-67	1	NaN
27	Tm169	Collagen-1	1	NaN
28	Er170	CD3	1	NaN
29	Yb171	HistoneH3	1	1.0
30	Yb172	Beta-Catenin	1	NaN
31	Yb173	CD45RO	1	NaN
32	Yb174	HLA-DR	1	NaN
33	BCKG190	190BCKG	0	NaN
34	Ir191	DNA1	1	1.0
35	Ir193	DNA2	1	1.0
36	Pt195	Seg1	1	2.0
37	Pt196	Seg2	1	2.0
38	Pt198	Seg3	1	2.0

Cellpose Denoising

If you have a gpu configured in this environment, try setting gpu = True when calling the cellpose_segment method

'''
Set the parameters for a Cellpose denoising!
'''
model_options = ['denoise_cyto3', 'deblur_cyto3', 'upsample_cyto3', 'denoise_nuclei', 'deblur_nuclei', 'upsample_nuclei']
model = model_options[0]

channel_list = [19, 23]      #### either a list of integers, with the integer corresponding to the layer of the image to denoise, or ["all"] to denoise all channels
                        ## here we denoise GFAP (21) and NeuN (20). See the index of experiment.panel[experiment.panel['keep'] == 1].reset_index().drop('index', axis = 1) file for the integer :: channel correspondence
                        ## This means that the numbers DO NOT match the original panel file! On the other hand, they do match the channel number if the image is opened in napari

image_folder = iSD_object.directory_object.img_dir + "/img"
output_directory = iSD_object.directory_object.img_dir + "/denoised_Collagen_Vitronectin"
img = ""   ## Empty string runs all images in image_folder

## specify img (a string of the filename -- not file path! -- of the image to only denoise that one image. This can be  useful as a test
            ## use os.listdir(image_folder) to retrieve a list of the possible values for img

img = os.listdir(image_folder)[0]   # Run 1 image

iSD_object.cellpose_denoise(gpu = False, model_type = model, diam_mean = 50.0, channel_list = channel_list,
                               image_folder = image_folder, output_folder = output_directory,
                               img = img)

CRC_1_ROI_001.ome.tiff denoising has been completed!

Cellpose Segmentation

If you have a gpu configured in this environment, try setting gpu = True when calling the cellpose_segment method

'''
Example of cellpose segmentation:
'''
image_folder = iSD_object.directory_object.img_dir + "/img"
output_folder = iSD_object.directory_object.masks_dir + "/cellpose"
img = ""   ## as with the cellpose denoising, specify a file name (not file path) to only segment one image in the image_folder
            ## an empty string, as here, denoises all files in image_folder

model_options = ["cyto3", "nuclei", "cyto2_cp3", "tissuenet_cp3", "livecell_cp3", "yeast_PhC_cp3",
                              "yeast_BF_cp3", "bact_phase_cp3", "bact_fluor_cp3", "deepbacs_cp3", "cyto2", "cyto",
                              "transformer_cp3", "neurips_cellpose_default", "neurips_cellpose_transformer",
                              "neurips_grayscale_cyto2"]
model_choice = "cyto3"

## diam_mean, flow_threshold, cellprob_threshold, and min_size are all arguments in the CellPose API --> see cellpose documentation for more details

iSD_object.cellpose_segment(gpu = False,
                                model_type = model_choice,
                                diam_mean = 50.0, image_folder = image_folder,
                                output_folder = output_folder,
                                img = img,
                                flow_threshold = 0.4,
                                cellprob_threshold = 0.0,
                                min_size = 15,
                                re_do = True)

CRC_1_ROI_001.ome.tiff has been Segmented with 1022 masks in the image!
CRC_1_ROI_002.ome.tiff has been Segmented with 1237 masks in the image!
CRC_2_ROI_001.ome.tiff has been Segmented with 1419 masks in the image!
CRC_2_ROI_002.ome.tiff has been Segmented with 680 masks in the image!
CRC_2_ROI_003.ome.tiff has been Segmented with 639 masks in the image!
CRC_2_ROI_004.ome.tiff has been Segmented with 123 masks in the image!
CRC_3_ROI_001.ome.tiff has been Segmented with 1965 masks in the image!
CRC_3_ROI_002.ome.tiff has been Segmented with 2421 masks in the image!
CRC_3_ROI_003.ome.tiff has been Segmented with 562 masks in the image!
CRC_3_ROI_004.ome.tiff has been Segmented with 1706 masks in the image!

masks = os.listdir(output_folder)
tf.imshow(tf.imread(output_folder + "/" + masks[0]))

(<Figure size 988.8x604.8 with 2 Axes>,
 <Axes: >,
 <matplotlib.image.AxesImage at 0x24247709d20>)

Mask Expansion

'''
Example of mask expansion:
'''
distance = 5              ###3 number of pixles to expand by
image_folder = iSD_object.directory_object.masks_dir + "/cellpose"
output_folder = iSD_object.directory_object.masks_dir + "/cellpose_expanded_5"
iSD.mask_expand(distance, image_source = image_folder, output_directory = output_folder)

masks = os.listdir(output_folder)
tf.imshow(tf.imread(output_folder + "/" + masks[0]))

(<Figure size 988.8x604.8 with 2 Axes>,
 <Axes: >,
 <matplotlib.image.AxesImage at 0x24247b9df00>)

‘Simple’ Denoising

'''
Example of simple denoising:
'''
input_folder = iSD_object.directory_object.img_dir + "/img"
output_folder = iSD_object.directory_object.img_dir + "/simple_denoised_Collagen_Vitronectin"
channel_list = [23]
sigma_range = None     ## or a list of floats of sigmas to calibrate nl_means_denoising on ('best' sigma from j-invariate calibration then use for a finer-tuning calibration, whose output is finally used to denoise)
pre_cal = False        ## when False, the simple denoiser sigmas are calibrated on cal_img. If True, then assumes this has alreayd been done by Experiment.simple_denoise_executor.calibrate_on_img()
cal_img = 0            ## an integer, denoting which image in the dataset to do the initial calibraiton on. As in --> os.listdir(folder_path)[cal_img]

iSD_object.simple_denoise(input_folder, output_folder, channel_list, sigma_range = sigma_range, pre_cal = False, cal_img = 0)

C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_1_ROI_001.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_1_ROI_002.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_2_ROI_001.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_2_ROI_002.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_2_ROI_003.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_2_ROI_004.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_3_ROI_001.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_3_ROI_002.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_3_ROI_003.ome.tiff denoising of channel 23 has been completed!
C:/Users/Default/Desktop/Example_IMC/images/simple_denoised_Collagen_Vitronectin/CRC_3_ROI_004.ome.tiff denoising of channel 23 has been completed!