This material is not actively maintained. For up-to-date information on the latest open-source tools for spatial biology, please refer to the repositories and/or documentation for the projects themselves, e.g.
This material is intended to help users become acclimated with the DeepCell ecosystem. DeepCell addresses three key needs for deep learning and biological images:
- How can I use deep learning easily on my data?
- How can I interact with these predictions?
- How can I improve these model predictions?
This tutorial will provide a gentle introduction to all three areas. Additionally, we have included a "Getting Started" section for users that may be unfamiliar with the basic tools covered in this material.
- Required software installations
- Intro to Unix, Docker, and Git
- Python best practices
- Basic Python, Numpy, and Scipy exercises
- Intro to Python image processing for live-cell imaging
- Intro to deep learning with tensorflow
Analyzing my images with pre-trained models
- Summary of available models
- Running pre-trained models in the cloud
- Running pre-trained models locally
Labeling my data with DeepCell Label
- Load files
- Use DeepCell Label
Building new and improved models with deepcell-tf
- Introduction to
deepcell-tf - Training a model in Google Colab
To learn more about the various systems and software that comprise DeepCell, please refer to the publications below. Relevant links are highlighted below each publication.
- The TissueNet dataset is available at deepcell.datasets
- The Mesmer pipeline can be run from deepcell.org or via ImageJ/QuPath plugins by reading our documentation on running pretrained models in the cloud
- The Mesmer pipeline can be run locally via a jupyter notebook as shown in this example notebook, or from the command line using our application Docker
- The ark-analysis multiplexed image analysis pipeline is at this github link
- All code for model training and figure generation for the paper can be found in our publication figures repo
Bannon et al. DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes
- The DeepCell Kiosk can be downloaded from the github repo and additional documentation is hosted on Read the Docs
- A persistent deployment of the Kiosk is hosted at https://deepcell.org/
- The code used to generate figures from the paper is available in our publication figure repo
Copyright © 2016-2021 The Van Valen Lab at the California Institute of Technology (Caltech), with support from the Shurl and Kay Curci Foundation, Google Research Cloud, the Paul Allen Family Foundation, & 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.
