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Deep learning for fluorescent spot detection

Project description

DeepCell Spots

Build Status Coverage Status Modified Apache 2.0 PyPI version PyPi Monthly Downloads Python Versions

deepcell-spots is a deep learning library for fluorescent spot detection image analysis. It allows you to apply pre-existing models and train new deep learning models for spot detection. It is written in Python and built using TensorFlow, Keras and DeepCell.

DeepCell-Spots for Developers

Build and run a local docker container, similarly to the instructions for deepcell-tf. The relevant parts are copied here with modifications to work for deepcell-spots. For more elaborate instructions, see the deepcell-tf README.

Build a local docker container, specifying the deepcell version with DEEPCELL_VERSION

git clone https://github.com/vanvalenlab/deepcell-spots.git
cd deepcell-spots
docker build --build-arg DEEPCELL_VERSION=0.12.0-gpu -t $USER/deepcell-spots . 

Run the new docker image

# '"device=0"' refers to the specific GPU(s) to run DeepCell-Spots on, and is not required
docker run --gpus '"device=0"' -it \
-p 8888:8888 \
$USER/deepcell-spots

It can also be helpful to mount the local copy of the repository and the notebooks to speed up local development.

# you can now start the docker image with the code mounted for easy editing
docker run --gpus '"device=0"' -it \
    -p 8888:8888 \
    -v $PWD/deepcell-spots/deepcell_spots:/usr/local/lib/python3.6/dist-packages/deepcell_spots \
    -v $PWD/notebooks:/notebooks \
    -v /$PWD:/data \
    $USER/deepcell-spots

DeepCell Spots Application

deepcell-spots contains an application that greatly simplifies the implementation of deep learning models for spot detection. deepcell-spots.applications contains a pre-trained model for fluorescent spot detection on images derived from assays such as RNA FISH and in-situ sequencing. This model returns a list of coordinate locations for fluorescent spots detected in the input image.

How to Use

from deepcell_spots.applications import Polaris

app = Polaris()
# image is an np array with dimensions (batch,x,y,channel)
# threshold is the probability threshold that a pixel must exceed to be considered a spot
coords = app.predict(image,threshold=0.9)

Copyright

Copyright © 2019-2022 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.

License

This software is licensed under a modified APACHE2. 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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Source Distribution

DeepCell-Spots-0.3.1.tar.gz (62.3 kB view hashes)

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