Deep learning for fluorescent spot detection
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=0.11.0-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 © 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.
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