AMyGDA

Description

This is a python3 module that takes a photograph of a 96 well plate and assesses each well for the presence of bacterial growth (here Mycobacterial tuberculosis). Since each well contains a different concentration of a different antibiotic, the minimum inhibitory concentration, as used in clinical microbiology, can be determined.

The development of this software was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) to aid the CRyPTIC project, but the software is agnostic to the plate design so could be easily adopted to other types or designs of 96-well (or even 384-well) plates.

Example image of a 96-well plate that has been analysed by AMyGDA. A yellow circle means the software has classified that well as containing bacterial growth.

Citing

preprint describing the software and demonstrating its reproducibility and accuracy is available from the biorXiv and the manuscript has been submitted to a peer-reviewed journal. Until it is accepted, please cite the biorXiv paper below.

Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis
Philip W Fowler, Ana Luiza Gibertoni Cruz, Sarah J Hoosdally, Lisa Jarrett, Emanuele Borroni, Matteo Chiacchiaretta, Priti Rathod,  Sarah Lehmann, Nikolay Molodtsov, Timothy M Walker, Esther Robinson, Harald Hoffmann, Timothy EA Peto, Daniela Maria M. Cirillo, E Grace Smith, Derrick W Crook
Microbiology (2018) in press; doi: https://doi.org/10.1099/mic.0.000733 (open access)

Installation

This is python3; python2 will not work. Installation is straightforward using the included setup.py script. Once you have downloaded the zip file, unpack it via

$ unzip amygda-1.3.0.zip 
$ cd amygda-1.3.0

Then install using the included setup.py script.

$ python setup.py install --user

The setup.py will automatically looks for the required following python packages and, if they are not present, will install them, or if they are an old version, will update them.

The information below is only included in case this process does not work. The prerequisites are

numpy and scipy.

Your python installation often includes numpy and scipy. To check, issue the following in a terminal

  $ python -c "import numpy"
  $ python -c "import scipy"

If you see an error, indicating numpy and/or scipy is not installed, please install the scipy stack by following these instructions.

matplotlib.

If your python installation includes numpy and scipy, there is a good chance it also includes matplotlib. Again to check

  $ python -c "import matplotlib" 

You can find installation instructions here.

opencv-python.

This can be installed using standard python tools, such as pip

  $ pip install opencv-python

AMyGDA was developed and tested using version 3.4.0 of OpenCV. If you do not have sudo access on your machine you can install this (and any other python module) in your $HOME directory using the following command

  $ pip install opencv-python --user
datreant.

This provides a neat way of storing and discovering metadata for each image using the native filesystem. It is not essential for the operation of AMyGDA, but the code would need re-factoring to remove this dependency. Again it can be installed using pip

  $ pip install datreant.core  

Note that datreant works best if each image is containing within its own folder. datreant automatically stores all metadata associated with each image within a JSON file in the same location as the input file.

Tutorial

The code is structured as a python module; all files for which can be found in the amygda/ subfolder.

ls
LICENCE.md                   amygda/                       setup.py
README.md                    examples/                     bin/
config/

You may see other folders like build/ if you are run the setup.py script. To run the tutorial move into the examples/ sub-folder

$ cd examples/
$ ls
sample-images/

analyse-plate-with-amygda.py is a simple python file showing how the module can be used to analyse a single image. The fifteen images shown in Figure S1 in the Supplement of the accompanying paper (see above) are provided so you can reconstruct Figures S2, S3, S4 & S12. The images are organised as follows

$ ls sample-images/
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 
$ ls sample-images/01/
image-01-raw.png

To process and analyse a single image using the default settings is simply

$ analyse-plate-with-amygda.py --image sample-images/01/image-01-raw.png 

And should take no more than 10 seconds. No output is written to the terminal, instead you will find a series of new files have been written in the samples-images/01 folder.

$ ls sample-images/01/
PlateMeasurement.0460f5d7-f3b2-45f7-9cdc-2bd407ba0790.json
image-01-arrays.npz
image-01-mics.txt
image-01-msf.jpg
image-01-clahe.jpg
image-01-filtered.jpg
image-01-growth.jpg
image-01-raw.png
  • The JSON file contains all the MICs and other metadata about the plate and can be automatically discovered and read using the datreant module to make systematic analyses simpler.
  • image-01-mics.txt contains the same information as the JSON file but in a simpler format that is easier for humans to read.
  • image-01-arrays.npz contains a series of numpy arrays that specify e.g. the percentage growth in each well
  • image-01-raw.png is the original image of the plate.
  • image-01-msf.jpg is a JPEG of the plate following mean shift filtering
  • image-01-clahe.jpg is a JPEG of the plate following mean shift filtering and then a Contrast Limited Adaptive Histogram Equalization filter to improve contrast and equalise the illumination across the plate.
  • image-01-filtered.jpg is a JPEG of the plate following both the above filtering operations and a histogram stretch to ensure uniform brightness.
  • image-01-growth.jpg adds some annotation; specifically the locations of the wells are drawn, each well is labelled with the name and concentration of drug and wells which AMyGDA has classified as containing bacterial growth are highlighted with a coloured circle.

To see the other options available for the analyse-plate-with-amygda.py python script

$ ./python analyse-plate-with-amygda.py --help
usage: analyse-plate-with-amygda.py [-h] [--image IMAGE]
                                [--growth_pixel_threshold GROWTH_PIXEL_THRESHOLD]
                                [--growth_percentage GROWTH_PERCENTAGE]
                                [--measured_region MEASURED_REGION]
                                [--sensitivity SENSITIVITY]
                                [--file_ending FILE_ENDING]

optional arguments:
  -h, --help        show this help message and exit
  --image IMAGE     the path to the image
  --growth_pixel_threshold GROWTH_PIXEL_THRESHOLD
                    the pixel threshold, below which a pixel is
                    considered to be growth (0-255, default=130)
  --growth_percentage GROWTH_PERCENTAGE
                    if the central measured region in a well has 
                    more than this percentage of pixels labelled as 
                    growing, then the well is classified as growth 
                    (default=2).
  --measured_region MEASURED_REGION
                    the radius of the central measured circle, as a
                    decimal proportion of the whole well 
                    (default=0.5).
  --sensitivity SENSITIVITY
                    if the average growth in the control wells is
                    more than (sensitivity x growth_percentage),
                    then consider growth down to this sensitivity 
                    (default=4)
  --file_ending FILE_ENDING
                    the ending of the input file that is stripped.
                    Default is '-raw'

To analyse all plates, you can either use a simple bash loop

 $ for i in 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15; do analyse-plate-with-amygda.py --image sample-images/$i/image-$i-raw.png; done;

Alternatively if you have GNU parallel installed you can use all the cores on your machine to speed up the process.

 $ find sample-images/ -name '*raw.png' | parallel analyse-plate-with-amygda.py --image {}   

To delete all the output files, thereby returning sample-images/ to its clean state, a bash script is provided. Use with caution!

$ cd samples-images/

$ ls 01/
PlateMeasurement.1d6f5af8-6005-4bb9-93cc-3390cefebfe4.json 
image-01-mics.txt                image-01-msf.jpg
image-01-arrays.npz              image-01-growth.jpg                          
image-01-clahe.jpg               image-01-raw.png                         
image-01-filtered.jpg                                      

$ bash remove-output-images.sh

$ ls 01/
image-01-raw.png

Adapting for different plate designs

AMyGDA is written to be agnostic to the particular design of plate, or even the number of wells on each plate. The concentration (or dilution) of drug in each well is defined by a series of plaintext files in

plate-configuration/

For example the drugs on the CRyPTIC1 V1 plate is defined within the

plate-configuration/CRyPTIC1-V1-drug-matrix.txt

file and looks like.

BDQ,KAN,KAN,KAN,KAN,KAN,ETH,ETH,ETH,ETH,ETH,ETH
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,AMI,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,RFB,PAS
BDQ,EMB,EMB,INH,LEV,MXF,DLM,LZD,CFZ,RIF,POS,POS 

Adding a new plate design is simply a matter of creating new files specifying the drug, concentration and dilution of each well. Note that changing the number of wells at present also involves specifying the well_dimensions when creating a PlateMeasurement object. Currently this defaults to (8,12) i.e. a 96-well plate in landscape orientation.

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