Interactive Deep Learning Tool for Cell Segmentation and Analysis

A tool to enable biologists to train machine learning models for their own pathology images.

Open the example projects in the client to learn how to use the software.

The code is released under the MIT license.

Client

Windows and macOS binaries are available in the releases area at the top right.

Requirements

• Win / macOS / Linux (cross-compilation for Android and iOS should work, too)
• Qt 5.14
• QtCreator or qmake

You also need to create an SSL certificate (core/data/luminosus_websocket.cert and core/data/luminosus_websocket.key). Please use the standard commands you can find on the Internet to create those.

Build Instructions

• git submodule update --init --recursive
• open src/luminosus-microscopy.pro in Qt Creator, configure and hit the green play button

Server

Requirements

• Python 3 and dependencies as listed in server/requirements.txt (most notably fast.ai and Flask)
• Nvidia GPU recommended for accelerated training and inference

Set-up

• copy SSL certificate from client in case you want to use HTTPS
• (optionally) create and activate Conda environment
• change to the server directory
• install Python dependencies (e.g. using conda install --file requirements.txt)
• run export FLASK_APP=main.py
• start the server with python3 -m flask run --host='::' --port=55712 to listen for IPv6 HTTP requests
• alternatively run python3 -m flask run --host='::' --port=55712 --cert=luminosus_websocket.cert --key=luminosus_websocket.key for HTTPS
• enter the IP address of the server in the clients settings, it will connect to it automatically