deepOrganoid: A deep learning model to predict cellular viability in organoid cultures

Directory structure:

./datasets		Pointer to an training, growth, and testing datasets

./Example_Data		a small dataset to run on

./env			Python environment for inference/deployment

./Models		Pointer to external directory hosting the pre-trained deepOrganoid model exported from DLS

./scripts		Scripts/code used for deploying the deepOrganoid model

Run deepOrganoid	Jupyter notebook script to run the deepOrganoid models
LICENSE			Licensing infromation
README.md		This file

Description of the training dataset

The deepOrganoid dataset consists of 3,456 datapoints with raw brightfield images, z-project brightfield images, and biochemical "annotations" from processing the assat plates with CellTiter Glow3D after imaging. In total the dataset if from 9 patient derived colorectal organoids treated with 9 drugs in 8-point dose response with 4 technical replicates per-plate. Images were acquired on an imageXpress Microconfocal.

Setting up the python environment

Download and install Anaconda/Python 3.6+
Download the python envirnment yaml file in /env
for CPU deployments run the following command in the Anaconda prompt:

conda env create --name DLS_deployment_cpu --file env_windows_cpu.yaml

for GPU deployments run the following command in the Anaconda prompt:

conda env create --name DLS_deployment_gpu --file env_windows_gpu.yaml

After installed, activate the envirnoment using the following command:

conda env activate DLS_deployment_gpu

or activate the DLS_deployment_gpu environment using Anaconda navigator.

Citation

RT Powell et al "deepOrganoid: A bright-field cell viability model for screening matrix embedded organoids"