kundan1974/rootpainter3d

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

RootPainter3D

This software is not approved for clinical use. Also please See LICENSE file.

Described in the paper "RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy"

Currently available at: https://arxiv.org/pdf/2106.11942.pdf

And now a published version is available at: http://doi.org/10.1002/mp.15353

Server setup

For the next steps I assume you have a suitable GPU and CUDA installed. A recent version of python 3 is required.

1. Clone the RootPainter3D code from the repository and then cd into the trainer directory (the server component).

git clone https://github.com/Abe404/RootPainter3D.git
cd RootPainter3D/trainer

2. To avoid alterating global packages. I suggest using a virtual environment. Create a virtual environment

python -m venv env

And then activate it.

On linux:

source ./env/bin/activate

On windows:

env\Scripts\activate.bat

3. Install PyTorch using pip by following the instructions at the pytorch website

4. Install the other dependencies in the virtual environment

pip install -r requirements.txt

5. Run root painter. This will first create the sync directory.

python main.py

You will be prompted to input a location for the sync directory. This is the folder where files, including instructions are shared between the client and server. I will use ~/root_painter_sync

The RootPainter3D server will then create some folders inside ~/root_painter_sync and start watching for instructions from the client.

You should now be able to see the folders created by RootPainter (datasets, instructions and projects) inside ~/root_painter_sync on your local machine.

Client setup

1. Clone the RootPainter3D code from the repository and then cd into the painter directory (the client component).

git clone https://github.com/Abe404/RootPainter3D.git
cd RootPainter3D/painter

2. To avoid alterating global packages. I suggest using a virtual environment. Create a virtual environment

python -m venv env

And then activate it.

On linux:

source ./env/bin/activate

On windows:

env\Scripts\activate.bat

3. Install the other dependencies in the virtual environment

pip install -r requirements.txt

4. Run the client.

fbs run

To interactively train a model and annotate images, you will need to add a set of compressed nifty images (.nii.gz) to a folder in the datasets folder and then create a project using the client that references this dataset.

For more details see the paper: Pre-print vailable at: https://arxiv.org/pdf/2106.11942.pdf And now a published version is available at: http://doi.org/10.1002/mp.15353