This is the Code Repository for the MICCAI-PRIME 2024 paper "Modeling the Neonatal Brain Development Using Implicit Neural Representations".
Check out our Project Page!
We recommend using miniforge3 to
install the necessary python version in a new mamba environment
using the conda_envs.yaml file, with
mamba env install -n braindev -f conda_envs.yaml
You can then activate it with
mamba activate braindev
You can configure the training or inference using config.yaml. The default
values are all set in the defaults.yaml file. To run it, use
python main.py config=config.yaml
You can add additional configurations on the command line to overwrite
the settings from the config files. To run the inference, you need to define a
start_epoch, the log_dir from the trained model,
change the mode if you want to use a different dataset and
specify inference=true.
The code assumes that you specify the three different database_*-csv files for the
e.g. training and inference. In this repo, we provide a sample with three entries.
For inference, we assume that each subject_id has scans from two distinct session_ids.
We fit the latent vector to one of those scans, and then evaluate the model at the age of the
other scan to compare.
For the implementation of the activations we are grateful that we could rely on the implementation of Neural Implicit Segmentation Functions. Please check out their paper in MICCAI 2023!
The dataset we used was the dHCP dataset (third release).
We've published the preprint on arXiv:
@article{bieder2024modeling,
title={Modeling the Neonatal Brain Development Using Implicit Neural Representations},
author={Bieder, Florentin and Friedrich, Paul and Corbaz, H{\'e}l{\`e}ne and Durrer, Alicia and Wolleb, Julia and Cattin, Philippe C},
journal={arXiv preprint arXiv:2408.08647},
year={2024}
}