Package: amygda
Automated Mycobacterial Growth Detection Algorithm (AMyGDA)This is apython3module that takes a photograph of a 96 well plate and assesses each well for the presence of bacterial growth (hereMycobacterial tuberculosis). Since each well contains a different concentration of a different antibiotic, the minimum inhibitory concentration, as used in clinical microbiology, can be determined.Apaperdescribing the software and demonstrating its reproducibility and accuracy is available from Microbiology.The development of this software was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) to aid theCRyPTIC project.Philip W Fowlerphilip.fowler@ndm.ox.ac.uk27 January 2020CitingPlease citeAutomated 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, Clara Grazian, Timothy M Walker, Esther Robinson, Harald Hoffmann, Timothy EA Peto, Daniela Maria M. Cirillo, E Grace Smith, Derrick W Crook
Microbiology (2018) 164:1522-1530 doi:10.1099/mic.0.000733InstallationThis is python3; python2 will not work. Installation is straightforward using the includedsetup.pyscript. First clone the repository (or download it directly from this GitHub page)$ git clone https://github.com/philipwfowler/amygda.gitThis will download the repository, creating a folder on your computer calledamygda/. If you only wish to install the package in your$HOMEdirectory (or don't have sudo access) issue the--userflag$ cd amygda/
$ python setup.py install --userAlternatively, to install system-wide$ sudo python setup.py installThe 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 arenumpyandscipy. 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, indicatingnumpyand/orscipyis not installed, please install the scipy stack by followingthese 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 instructionshere.opencv-python. This can be installed using  standard python tools, such as pip$ pip install opencv-pythonAMyGDAwas developed and tested using version 3.4.0 ofOpenCV. If you do not havesudoaccess on your machine you can install this (and any other python module) in your$HOMEdirectory using the following command$ pip install opencv-python --userdatreant. This provides a neat way of storing and discovering metadata for each image using the native filesystem. It is not essential for the operation ofAMyGDA, but the code would need re-factoring to remove this dependency. Again it can be installed using pip$ pip install datreantNote thatdatreantworks best if each image is containing within its own folder.datreantautomatically stores all metadata associated with each image within twoJSONfiles in a hidden.datreantfolder in the same location as the input file.TutorialThe code is structured as a python module; all files for which can be found in theamygda/subfolder.$ ls
LICENCE.md                   amygda/                       setup.py
README.md                    examples/(You may see other folders likebuild/if you are run thesetup.pyscript. To run the tutorial move into theexamples/sub-folder.$ cd examples/
$ ls
analyse-plate-with-amygda.py plate-configuration/          sample-images/analyse-plate-with-amygda.pyis 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.pngTo process and analyse a single image using the default settings is simply$ analyse-plate-with-amygda.py --image sample-images/01/image-01-raw.pngAnd 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 thesamples-images/01folder.$ ls -a sample-images/01/
.datreant/
image-01-arrays.npz
image-01-filtered.png
image-01-mics.txt
image-01-processed.png
image-01-raw.pngThe hidden.datreant/folder contains twoJSONfiles.categories.jsoncontains all the MICs and other metadata about the plate and both can be automatically discovered and read using thedatreantmodule to make systematic analyses simpler.image-01-mics.txtcontains the same information as theJSONfile but in a simpler format that is easier for humans to read.image-01-arrays.npzcontains a series ofnumpyarrays that specify e.g. the percentage growth in each wellimage-01-raw.pngis the original image of the plate.image-01-msf.jpgis a JPEG of the plate following mean shift filteringimage-01-clahe.jpgis 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-final.jpgis a JPEG of the plate following both the above filtering operations and a histogram stretch to ensure uniform brightness.image-01-growth.pngadds 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 theanalyse-plate-with-amygda.pypython script$ 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 haveGNU parallelinstalled you can use all the cores on your machine to speed up the process.$ find sample-images/ -name '*raw.png' | parallel --bar 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/
image-01-mics.txt
image-01-arrays.npz                                        image-01-clahe.jpg
image-01-filtered.jpg                                      image-01-raw.png
image-01-growth.jpg					   image-01-msf.jpg

$ bash remove-output-images.sh

$ ls 01/
image-01-raw.pngLicenceThe software is available subject to the terms of the attached academic-use licence.Adapting for different plate designsAMyGDA 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 inconfig/For example the drugs on the UKMYC5 plate is defined within theconfig/UKMYC5-drug-matrix.txtfile 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,POSAdding a new plate design is simply a matter of creating new files specifying the drug, concentration and dilution of each well. Note that changing thenumberof 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. As an example, the configuration files for the UKMYC6 plate, which is the successor to the UKMYC5 plate, are included although all the provided examples are of UKMYC5 plates.