We present a pipeline for unbiased and robust multimodal registration of neuroimaging modalities with minimal preprocessing. While typical multimodal studies need to use multiple independent processing pipelines, with diverse options and hyperparameters, we propose a single and structured framework to jointly process different image modalities. The use of state-of-the-art learning-based techniques enables fast inferences, which makes the presented method suitable for large-scale and/or multi-cohort datasets with a diverse number of modalities per session.
- Clone this repository.
- Create a virtual environment using virtualenv or conda. We reccommend using Python>=3.8.
conda create -n jump-env
python -m venv jump-env - Install the required dependencies under requirements.txt:
pip install -r requirements.txt - You need freesurfer (v7.4+) installed.
The
15/11/2023: JUMP is up and running
Initial commit of JUMP pipeline and documentation
A joint multimodal registration pipeline for neuroimaging with minimal preprocessing
Adria Casamitjana, Juan Eugenio Iglesias, Raul Tudela, Aida Niñerola-Baizan, Roser Sala-Llonch
Submitted to ISBI'24
[ article | arxiv | bibtex ]