cellml 0.1.1

Integrated tool to measure the shapes of adherent cells on posts.

CellML

Disclaimer: This program is undergoing active development and should not be used for production. All APIs and commands are susceptible to change without notice.

Integrated tool to segment individual cells from optical cell images and use machine learning to determine whether cells are adherent or not based on their shapes.

From a directory containing a time-series of images of cells on surfaces, the tool segments individual cells and uses a pre-trained CNN model to determine whether each cell is adherent or not.

Installation

Install with pip

CellML is on PyPI so it can be installed with pip

pip install cellml

Install from source

Clone the repository to your computer

git clone https://github.com/caromccue/CellML.git

and install with pip

cd CellML
pip install .

Usage

Quickstart

A time series of images of cells on surfaces must be stored in a directory prior to usage of CellML The application can then be used to process the images as follows:

cellml process --save-plot path/to/directory

cellml process command

The process command takes a directory of images, segments the cells in each image and determines how many cells are adherent or detached from the surface. The program saves a .csv file at the root of that directory with the name of each image, the time it was taken (from EXIF data) and the number of cells (total, adherent and detached).

Arguments

• -c, --check-segmentation displays the result of segmenting an image (selected at approximately 80% of the time series) to verify that the segmentation algorithm works well before processing.
• -o, --save-overlay resaves all images in the directory with an overlay showing detected cells in red (adherent) or green (detached) for process control.
• -p, --save-plot generates and saves plots of cell detachment over time
• -v, --verbose increases the verbosity level

cellml segment command

The segment command runs the segmentation algorithm on an image or a directory of images and saves the segmented cell images to disk.

Arguments

• -o, --save-overlay resaves all images in the directory with an overlay showing detected cells
• -v, --verbose increases the verbosity level
• -p, --postsize selects the size of microposts on the surfaces in the images to set the segmentation parameters accordingly

cellml train command

The train command is used to train the machine learning models used to label the segmented cells as adherent or not. A directory of training data is expected containing subdirectories named Detached and Adherent containing grayscale images of segmented cells (use the segment command to generate the images).

Arguments

• -m, --model selects the type of model to train (svm|cnn|cnn-transfer)
• -tb, --tensorboard saves logs for tensorboard visualization in <cwd>/logs
• -v, --verbose increases the verbosity level

Repository structure

• models: pre-trained machine learning models for cell adhesion discrimination
• notebooks: jupyer notebooks evaluating different image segmentation strategies
• src: source code for the project
  • cell_processing: processing pipeline from directory to detachment rate
  • data: data processing methods, including segmentation and extraction
  • models: model definition and training scripts for the cell binary labelling task
  • visualization: visualization and plotting methods
  • cli.py: entry point to the command line interface
• tests: unittesting

License

This project is licensed under the GPLv3 License - see the LICENSE.md file for details.

Credit

Initial models were built starting from the example at: https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

Live data visualization class TrainingPlot originally from: https://github.com/kapil-varshney/utilities/blob/master/training_plot/trainingplot.py