jcasado

# Instructions to run this on one sample:
# conda env create -n quant -f quantification.yml
# conda activate quant
# python quantification.py -M /path/to/cellMask.tif -in /path/to/Sample_01.ome.tif -o /path/to/output/ -ch /path/to/channel_names.csv -c 46
# <julia.casado@helsinki.fi>
import os
import argparse
from pathlib import Path
import csv
import time

jcasado

% Crop TMA ome.tif into the already detected cores
% Quantify with already computed masks.
% /casado

basePath = 'D:\path\to\the\project\';
maskPath = 'segmentation';
maskFileName = 'cellRingMask.tif';
omePath = 'registration';
omeSuffix = '.ome.tif';
cropCoordsPath = 'dearray';

jcasado

// Prepare file formats for segmentation workflow: Ilastik + Cell Profiler + HistoCAT
// Input must be a folder with multipage TIFF files, one for each sample or core
// 19.6.2019 Julia and Nuppu

basePath = "/path/to/project/folder/";
coreStacks = "tiff/files/folder/";

for_histocat = "for_histocat/"; //Where to save Image sequences
for_ilastik = "for_ilastik/"; //Where to save .h5 files

jcasado

import os
import pandas as pd
import cycifsuite.detect_lost_cells as dlc
import numpy as np
import time
import glob
from multiprocessing import Process, freeze_support

def processFile(fname, path_out, manual_threshold=None):
    t = time.time()

jcasado

import glob
import os
import pandas as pd
import numpy as np
import time
import skimage.io

pathIn = "Z:/Connor/Topacio_P2_AF/ashlar"
pathOut= "D:/julia/cell_filter/"

jcasado

%% 1. Input using Bio-formats

% Read image into 3D array and resize to be easy to handle
filePath = "tma_big.tif";
image = bfOpen3DVolume(filePath);
image= image{1}{1};
nuclei = image(:,:,1);
imagesub = imresize(nuclei,0.02);

%% 2. Morphological operations