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. More detailed documentation is available here.
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 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
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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