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Deep Learning for Single Cell Analysis
Python
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willgraf Simplify RetinaNetGenerator (#217)
* remove include_mask_transforms.

* use the full key instead of the substring.

* insert transformed y_semantic into self.y_semantic_list, don't duplicate lists

* no need to check panoptic, if the list is empty nothing to iterate over.

* define batch_y_semantic_list whether or not panoptic.

* remove panoptic checks, use empty lists in general solution.
Latest commit 09a7b10 Sep 11, 2019

README.md

DeepCell: Deep Learning for Single Cell Analysis

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DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

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

Examples

Raw Image Segmented and Tracked

Getting Started

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

Pixel-Wise Segmentation

Deep Watershed Instance Segmentation

Cell Tracking in Live Cell Imaging

DeepCell for Developers

DeepCell uses nvidia-docker and 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 TF_VERSION.

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

Copyright

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.

License

This software is licensed under a modified APACHE2.

License

See LICENSE for full details.

Trademarks

All other trademarks referenced herein are the property of their respective owners.

Credits

Van Valen Lab, Caltech

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