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Deep learning for fluorescent spot detection

Project description

DeepCell Spots

Build Status Documentation 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. More detailed documentation is available here.

Spot Detection Example

DeepCell Spots Application

deepcell-spots contains an applications that greatly simplify the implementation of deep learning models for spot detection. deepcell-spots.applications.SpotDetection 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. deepcell-spots.applications.Polaris pairs this spot detection model with DeepCell models for nuclear and cytoplasmic segmentation.

How to Use

from deepcell_spots.applications import SpotDetection

app = SpotDetection()
# 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)

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
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 \

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 \


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.


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.


Van Valen Lab, Caltech

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