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ConfluentFUCCI

A suite of tools for analyzing large scale confluent FUCCI experiments

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Installation

The recommended way for trying out ConfluentFUCCI is to use our prebuilt conainer image:

docker run -it --rm \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -p 8080:8080 \
    -p 9876:9876 \
    leogold/confluentfucci:latest

This will start a container that will serve ConfluentFUCCI on localhost:8080 and a virtual desktop on localhost:9876. The app served using the above command does not require a GPU, which significantly affects segmentation time. Too speed up segmentation by leveraging your CUDA compatible GPU, please use:

docker run -it --rm \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -p 8080:8080 \
    -p 9876:9876 \
    --gpus all \
    leogold/confluentfucci:latest

Using docker-compose

To simplify deployment, please check out our docker-compose.yaml. Placing this file in the same path as your data should allow you to test the app using:

docker compose up

If a CUDA compatible GPU is availble on your system, make sure to uncomment:

#    deploy:
#      resources:
#        reservations:
#          devices:
#            - driver: nvidia
#              count: 1
#              capabilities: [ gpu ]