VesselBoost is a Python-based software package utilizing deep learning techniques to segment high-resolution time-of-flight MRI angiography data, with high sensitivity towards small vessels. The software suite encompasses three essential functional modules: (1) predict, (2) test-time adaptation (TTA), and (3) boost. By leveraging these modules, users can efficiently segment high-resolution time-of-flight data or conveniently leverage our command line interface to boost segmentations for other vascular MRI image contrasts.
VesselBoost is a Python-based software package leveraging a UNet3D-based segmentation pipeline that utilizes data augmentation and test-time adaptation (TTA) to enhance segmentation quality and is generally applicable to high-resolution magnetic resonance angiograms (MRAs).
This repository contains 3 major modules:
- Predict. With this module, users can segment high-resolution time-of-flight using our pre-trained models. It can be used to generate intermediate proxy segmentations as well as the final ones.
- Test-time-adaptation. This module allows the user to provide a proxy segmentation or generate a proxy with our pre-trained model (Module 1), to drive further adaptation of the pre-trained models.
- Booster. Boost allows users to train a segmentation model on a single or more data using existing imperfect segmentation.
VesselBoost 0.9.4
- Docker / Singularity container
VesselBoost, pre-trained models, and required software are packaged in software containers available through Dockerhub and Neurodesk.
The Dockerhub container is available at Dockerhub. To download the container, run the following command:
docker pull vnmd/vesselboost_0.9.4
To predict vessel segmentation using your data and the latest version of VesselBoost on Neurodesk, you can run the following code snippet:
ml vesselboost
path_to_model=/cvmfs/neurodesk.ardc.edu.au/containers/vesselboost_0.9.4_20240404/vesselboost_0.9.4_20240404.simg/opt/VesselBoost/saved_models
prediction.py --ds_path /path/ --out_path /path/ --pretrained "$path_to_model"/manual_0429 --prep_mode 4
For more information, please check our notebooks.
This is a Python-based software package. To successfully run this project on your local machine, please follow the following steps to set up the necessary software environment.
- Clone this repository to your local machine
git clone https://github.com/KMarshallX/vessel_code.git
- Install miniconda:
cd vessel_code bash miniconda-setup.sh
- Then set your current working directory as the cloned repository, and install the remaining required packages
conda env create -f environment.yml conda activate vessel_boost
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Marshall Xu <[email protected]>
Saskia Bollmann <[email protected]>
Fernanda Ribeiro <[email protected]>