caactus 0.1.4

Package for pre- and post-processing of images and data for working with ilastik-software

caactus

caactus (cell analysis and counting tool using ilastik software) is a collection of python scripts to provide a streamlined workflow for ilastik-software, including data preparation, processing and analysis. It aims to provide biologist with an easy-to-use tool for counting and analyzing cells from a large number of microscopy pictures.

Introduction

The goal of this script collection is to provide an easy-to-use completion for the Boundary-based segmentation with Multicut-workflow in ilastik. This workflow allows for the automatization of cell-counting from messy microscopic images with different (touching) cell types for biological research. For easy copy & paste, commands are provided in grey code boxes with one-click C&P

Installation

Install miniconda, create an environment and install Python and vigra

• Download and install miniconda for your respective operating system according to the instructions.
  • Miniconda provides a lightweight package and environment manager. It allows you to create isolated environments so that Python versions and package dependencies required by caactus do not interfere with your system Python or other projects.
• Once installed, create an environment for using caactus with the following command from your cmd-line

  conda create -n caactus-env -c conda-forge "python>=3.10.12" vigra 
  

Install caactus

• Activate the caactus-env from the cmd-line with

  conda activate caactus-env
  

• To install caactus plus the needed dependencies inside your environment, use

  pip install caactus
  

• During the below described steps that call the caactus-scripts, make sure to have the caactus-env activated.

Install ilastik

• Download and install ilastik for your respective operating system.

Workflow

A Culturing

• Culture your cells in a flat bottom plate of your choice and according to the needs of the organims being researched.

B Image acquisition

• In your respective microscopy software environment, save the images of interest to .tif-format.
• From the image metadata, copy the pixel size and magnification used.

C Data Preparation

C.1 Create Project Directory

• For portability of the ilastik projects create the directory in the following structure:
  (Please note: the below example already includes examples of resulting files in each sub-directory)
• This allows you to copy an already trained workflow and use it multiple times with new datasets

project_directory  
├── 1_pixel_classification.ilp  
├── 2_boundary_segmentation.ilp  
├── 3_object_classification.ilp
├── renaming.csv
├── conif.toml
├── 0_1_original_tif_training_images
  ├── training-1.tif
  ├── training-2.tif
  ├── ...
├── 0_2_original_tif_batch_images
  ├── image-1.tif
  ├── image-2.tif
  ├── ..
├── 0_3_batch_tif_renamed
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1.tif
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2.tif
  ├── ..
├── 1_images
  ├── training-1.h5
  ├── training-2.h5
  ├── ...
├── 2_probabilities
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Probabilities.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Probabilities.h5
  ├── ...
├── 3_multicut
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Multicut Segmentation.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Multicut Segmentation.h5
  ├── ...
├── 4_objectclassification
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Object Predictions.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_table.csv
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Object Predictions.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_table.csv
  ├── ...
├── 5_batch_images
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2.h5
  ├── ...
├── 6_batch_probabilities
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Probabilities.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Probabilities.h5
  ├── ...
├── 7_batch_multicut
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Multicut Segmentation.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Multicut Segmentation.h5
  ├── ...
├── 8_batch_objectclassification
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_Object Predictions.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-1-data_table.csv
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_Object Predictions.h5
  ├── strain-xx_day-yymmdd_condition1-yy_timepoint-zz_parallel-2-data_table.csv
  ├── ...
├── 9_data_analysis

C.1 Setup config.toml-file

• copy config/config.toml to your working directory and modify it as needed.
• the caactus scripts are setup for pulling the information needed for running from the file
• in the following commands, replace \path\to\config.toml with your actual path
  • CAVE: for Windows users make sure to change the backlash fro /path/to/config.toml to \path\to\config.toml, when copying the path to your working directory
• open the command line and save the path to your project file to a variable
  • whole command UNIX:

  p = "\path\to\config.toml" 
  

• whole command Windows:

  $p = "\path\to\config.toml"
  

D Training

D.1. Selection of Training Images and Conversion

D.1.1 Selection of Training data

• select a set of images that represant the different experimental conditions best
• store them in 0_1_original_tif_training_images

D.1.2 Conversion

• call the tif2h5py script from the cmd prompt to transform all .tif-files to .h5-format. The .h5-format allows for better performance when working with ilastik.
• select "-c" and enter path to config.toml
• select "-m" and choose "training"
• whole command UNIX:

  tif2hpy -c "$p" -m training
  

• whole command Windows:

  tif2hpy.exe -c $p -m training
  

D.2. Pixel Classification

D.2.1 Project setup

• Follow the the documentation for pixel classification with ilastik.
• Create the 1_pixel_classification.ilp-project file inside the project directory.
• For working with neighbouring / touching cells, it is suggested to create three classes: 0 = interior, 1 = background, 2 = boundary (This follows python's 0-indexing logic where counting is started at 0).

D.2.2 Export Probabilties

In prediction export change the settings to

• Convert to Data Type: integer 8-bit
• Renormalize from 0.00 1.00 to 0 255
• File:

  {dataset_dir}/../2_probabilties/{nickname}_{result_type}.h5
  

D.3 Boundary-based Segmentation with Multicut

D.3.1 Project setup

• Follow the the documentation for boundary-based segmentation with Multicut.
• Create the 2_boundary_segmentation.ilp-project file inside the project directory.
• In DT Watershed use the input channel the corresponds to the order you used under project setup (in this case input channel = 2).

D.3.2 Export Multicut Segmentation

In prediction export change the settings to

• Convert to Data Type: integer 8-bit
• Renormalize from 0.00 1.00 to 0 255
• Format: compressed hdf5
• File:

  {dataset_dir}/../3_multicut/{nickname}_{result_type}.h5
  

D.4 Background Processing

For futher processing in the object classification, the background needs to eliminated from the multicut data sets. For this the next script will set the numerical value of the largest region to 0. It will thus be shown as transpartent in the next step of the workflow. This operation will be performed in-situ on all .*data_Multicut Segmentation.h5-files in the project_directory/3_multicut/.

• call the background-processing script from the cmd prompt
• select "-c" and enter path to config.toml
• enter "-m training" for training mode
• whole command UNIX:

  background_processing -c "$p" -m training
  

• whole command Windows:

  background_processing.exe -c $p -m training
  

D.5. Object Classification

D.5.1 Project setup

• Follow the the documentation for object classification.
• define your cell types plus an additional category for "not-usuable" objects, e.g. cell debris and cut-off objects on the side of the images

D.5.2 Export Object Information

In Choose Export Imager Settings change settings to

• Convert to Data Type: integer 8-bit
• Renormalize from 0.00 1.00 to 0 255
• Format: compressed hdf5
• File:

  {dataset_dir}/../4_objectclassification/{nickname}_{result_type}.h5
  

In Configure Feature Table Export General change seetings to

• format .csv and output directory File:

  {dataset_dir}/../4_objectclassification/{nickname}.csv`
  

• select your features of interest for exporting

E Batch Processing

• Follow the documentation for batch processing
• store the images you want to process in the 0_2_original_tif_batch_images directory
• Perform steps D.2 to D.5 in batch mode, as explained in detail below (E.2 to E.5)

E.1 Rename Files

• Rename the .tif-files so that they contain information about your cells and experimental conditions
• Create a csv-file that contains the information you need in columns. Each row corresponds to one image. Follow the same order as the sequence of image acquisition.
• the only hardcoded columns that have to be added are biorep for "biological replicate" and techrep for "technical replicate". They are needed for downstream analysis for calculating the averages
• The script will rename your files in the following format columnA-value1_columnB-value2_columnC_etc.tif eg. as seen in the example below picture 1 (well A1 from our plate) will be named strain-ATCC11559_date-20241707_timepoint-6h_biorep-A_techrep-1.tif
• Call the rename script from the cmd prompt to rename all your original .tif-files to their new name.
• whole command Unix:

  renaming -c "$p"
  

• whole command Windows:

  renaming.exe -c $p
  

E.2 Batch Processing Pixel Classification

• open the 1_pixel_classification.ilp project file
• under Prediction Export change the export directory to File:

  {dataset_dir}/../6_batch_probabilities/{nickname}_{result_type}.h5
  

• under Batch Processing Raw Data select all files from 5_batch_images

E.3 Batch Processing Multicut Segmentation

• open the 2_boundary_segmentation.ilp project file
• under Choose Export Image Settings change the export directory to File:

  {dataset_dir}/../7_batch_multicut/{nickname}_{result_type}.h5
  

• under Batch Processing Raw Data select all files from 5_batch_images
• under Batch Processing Probabilities select all files from 6_batch_probabilities

E.4 Background Processing

For futher processing in the object classification, the background needs to eliminated from the multicut data sets. For this the next script will set the numerical value of the largest region to 0. It will thus be shown as transpartent in the next step of the workflow. This operation will be performed in-situ on all .*data_Multicut Segmentation.h5-files in the project_directory/3_multicut/.

• call the background-processing.py script from the cmd prompt
• enter "-m batch" for batch mode
• whole command Unix:

  background_processing -c "$p" -m batch
  

• whole command Windows:

  background_processing.exe -c $p -m batch
  

E.5 Batch processing Object classification

• under Choose Export Image Settings change the export directory to File:

  {dataset_dir}/../8_batch_objectclassification/{nickname}_{result_type}.h5
  

• in Configure Feature Table Export General choose format .csv and change output directory to:

  {dataset_dir}/../8_batch_objectclassification/{nickname}.csv
  

• select your features of interest for exporting
• under Batch Processing Raw Data select all files from 5_batch_images
• under Batch Processing Segmentation Image select all files from 7_batch_multicut

F Post-Processing and Data Analysis

• Please be aware, the last two scripts, summary_statisitcs.py and pln_modelling.py at this stage are written for the analysis and visualization of two independent variables.

F.1 Merging Data Tables and Table Export

The next script will combine all tables from all images into one global table for further analysis. Additionally, the information stored in the file name will be added as columns to the dataset.

• call the csv_summary.py script from the cmd prompt
• whole command Unix:

  csv_summary -c "$p"
  

• whole command Windows

  csv_summary -c $p
  

• Technically from this point on, you can continue to use whatever software / workflow your that is easiest for use for subsequent data analysis.

F.2 Creating Summary Statistics

• call the summary_statistics.py script from the cmd prompt
• whole command Unix:

  summary_statistics -c "$p"
  

• whole command Windows:

  summary_statistics.exe -c $p
  

• if working with EUCAST antifungal susceptibility testing, call summary_statistics_eucast

F.3 PLN Modelling

• call the pln_modelling.py script from the cmd prompt`
• whole command Unix:

  pln_modelling -c "$p"
  

• whole command Windows:

  pln_modelling.exe -c $p
  

• please note: the limit of categories for display in the PCA-plot is n=15