DeepCell: Deep Learning for Single Cell Analysis
DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. The framework processes raw images and uniquely annotates each cell in the image. These annotations can be used to quantify a variety of cellular properties.
Read the documentation at deepcell.readthedocs.io
For more information on deploying DeepCell in the cloud refer to the DeepCell Kiosk documentation
|Raw Image||Segmented and Tracked|
The fastest way to get started with DeepCell is to run the latest docker image:
nvidia-docker run -it --rm -p 8888:8888 vanvalenlab/deepcell-tf:latest
This will start a jupyter session, with several example notebooks detailing various training methods:
PanOptic Segmentation using RetinaMask
Deep Watershed Instance Segmentation
Cell Tracking in Live Cell Imaging
DeepCell for Developers
tensorflow to enable GPU processing.
Build a local docker container
git clone https://github.com/vanvalenlab/deepcell-tf.git cd deepcell-tf docker build -t $USER/deepcell-tf .
The tensorflow version can be overridden with the build-arg
docker build --build-arg TF_VERSION=1.9.0-gpu -t $USER/deepcell-tf .
Run the new docker image
# NV_GPU refers to the specific GPU to run DeepCell on, and is not required # Mounting the codebase, scripts and data to the container is also optional # but can be handy for local development NV_GPU='0' nvidia-docker run -it \ -p 8888:8888 \ $USER/deepcell-tf:latest
It can also be helpful to mount the local copy of the repository and the scripts to speed up local development.
NV_GPU='0' nvidia-docker run -it \ -p 8888:8888 \ -v $PWD/deepcell:/usr/local/lib/python3.5/dist-packages/deepcell/ \ -v $PWD/scripts:/notebooks \ -v /data:/data \ $USER/deepcell-tf:latest
How to Cite
- The original DeepCell paper
- DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis
Copyright © 2016-2019 The Van Valen Lab at the California Institute of Technology (Caltech), with support from the Paul Allen Family Foundation, Google, & National Institutes of Health (NIH) under Grant U24CA224309-01. All rights reserved.
This software is licensed under a modified APACHE2.
See LICENSE for full details.
All other trademarks referenced herein are the property of their respective owners.